Training & Workshop Sessions
– Taught by World-Class Data Scientists –
Learn the latest data science concepts, tools, and techniques from the best. Forge a connection with these rock stars from industry and academia, who are passionate about molding the next generation of data scientists.
ODSC EUROPE Hybrid Conference 2024
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Beginner to Advanced Level Training
From the Leading Instructors in the Industry
Machine Learning
Federated Learning for Data Privacy
Explainable AI and Bias in machine learning
Machine Learning at Scale using Apache Spark
Automated Machine Learning (AutoML)
Causal Inference with Machine Learning
Deep Learning
Deep Learning with PyTorch & Tensorflow
Introduction to Deep learning
Deepfakes Tutorial
Deep Reinforcement learning
NLP
Transfer Learning in NLP
NLP Pre-trained Transformer Models with Bert, Ernie, and GPT-2
Hugging Face Transformer Library Workshop
Applications of NLP; Sentiment Analysis, Dialog Systems, and Semantic Search
Advanced Topics in NLP
ADDITIONAL TUTORIALS & WORKSHOPS
Machine Learninng for Stock Trading
An Introduction to Macine Learing for Finance
Quantitative Finance: Enhancement with Vector Search
Optimizing Healthcare with Vector Search
Beyond the Basics: Data Visualization in Python
Communicating Data Insights with Impact
Continuous Learning with On-Demand Training Sessions and Workshops
Learn from some of the best and brightest minds in data science and AI on the Ai+ Training platform featuring:
- Hands-on Training
- Skills Assessments
- Certification Exams
- ODSC Conference Recordings
- Webinars
- Deep Learning Bootcamp
Past Europe Instructors
Click on the toggle bars for information around sessions and instructors. Check full Speaker and instructor line-up here.

Matthias Seeger, PhD
Matthias W. Seeger is a principal applied scientist at Amazon. He received a Ph.D. from the School of Informatics, Edinburgh university, UK, in 2003 (advisor Christopher Williams). He was a research fellow with Michael Jordan and Peter Bartlett, University of California at Berkeley, from 2003, and with Bernhard Schoelkopf, Max Planck Institute for Intelligent Systems, Tuebingen, Germany, from 2005. He led a research group at the University of Saarbruecken, Germany, from 2008, and was assistant professor at the Ecole Polytechnique Federale de Lausanne from fall 2010. He joined Amazon as machine learning scientist in 2014. He received the ICML Test of Time Award in 2020.
His interests center around Bayesian learning and decision making with probabilistic models, from gaining understanding to making it work in large scale practice. He has been working on theory and practice of Gaussian processes and Bayesian optimization, scalable variational approximate inference algorithms, Bayesian compressed sensing, and active learning for medical imaging. More recently, he worked on demand forecasting, hyperparameter tuning (Bayesian optimization) applied to deep learning (NLP), and AutoML.
Distributed Hyperparameter Tuning: Finding the Right Model can be Fast and Fun(Tutorial)

Heiko Hotz
Heiko Hotz is a Senior Solutions Architect for AI & Machine Learning at AWS with a special focus on Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI. He is also the founder of the NLP London Meetup group, bringing together NLP enthusiasts and industry experts.
Implementing Generative AI in Organisations: Challenges and Opportunities(Tutorial)

Stefanie Molin
Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

Dr. Yves J. Hilpisch
Dr. Yves J. Hilpisch is founder and CEO of The Python Quants (http://tpq.io), a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading, and computational finance. He is also founder and CEO of The AI Machine (http://aimachine.io), a company focused on AI-powered algorithmic trading based on a proprietary strategy execution platform.
Yves has a Diploma in Business Administration, a Ph.D. in Mathematical Finance and is Adjunct Professor for Computational Finance at Miami Herbert Business School.

Andras Zsom, PhD
Andras Zsom is an Assistant Professor of the Practice and Director of Graduate Studies at the Data Science Initiative at Brown University, Providence, RI. He is teaching two mandatory courses in the data science master’s program, and helps the students navigate through their studies and curriculum. He also supervises interns on various research projects related to missing data, interpretability, and developing machine learning pipelines.

Oliver Zeigermann
Oliver Zeigermann has been developing software with different approaches and programming languages for more than 3 decades. In the past decade, he has been focusing on Machine Learning and its interactions with humans.
MLOps: Monitoring and Managing Drift(Training)

Guglielmo Iozzia
Guglielmo is a Biomedical Engineer with an extensive background in Software Engineering and Data Science applied to different contexts, such as Biotech Manufacturing, Healthcare and DevOps, just to mention the latest, and a lifelong learner. As part of the Manufacturing IT Advanced Mathematics and Modelling Data Science Team he is currently busy unlocking business value through Deep Learning projects, mostly in Computer Vision (not restricted to this field by the way). He has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference.
He is also an international speaker and author of the following book: Hands-on Deep Learning with Apache Spark @Packt https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-apache-spark

Elisa Fromont
Elisa Fromont is a full professor at Université de Rennes France, since 2017 and a Junior member of the Institut Universitaire de France (IUF). She works at IRISA research institute in the INRIA LACODAM (“Large Scale Collaborative Data Mining”) team. From 2008 until 2017, she was associate professor at Université Jean Monnet in Saint-Etienne, France. She worked at the Hubert Curien research institute in the Data Intelligence team. Elisa received her Research Habilitation (HDR) in December 2015 from the University of Saint-Etienne. Her research interests lie in (explainable) machine learning, data mining and, in particular, time series analysis.
Explainable Time Series Classification (Tutorial)

Dr.-Ing. Thomas Albin
Thomas is a Senior Machine Learning engineer, working in the automotive industry since 2019. Before joining the Research & Development department of a large manufacturer he was conducting research activities in space science. In parallel to his studies in Astro- and Geo-Physics and later PhD program, he participated in 2 major missions: ESA’s comet mission Rosetta/Philae and NASA’s & ESA’s Saturn spacecraft Cassini/Huygens; always with a special focus on cosmic dust. Additionally, he applies Machine Learning algorithms to analyse astronomy- and space-related data to derive new scientific insights or to create new methods for calibrating instruments. Besides his industry work, Thomas is a guest scientist at the Free University of Berlin, where he continues working on the Cassini-related datasets using Deep Learning. On his active YouTube channel Astroniz he shares his Python + Space Science + Machine Learning knowledge with a small community.
Space Science with Python – Enabling Citizen Scientists(Workshop)

Leonidas Souliotis, PhD
Leonidas (Leo) is a Senior Data Scientist at Astrazeneca. His work is focused around machine learning in oncology, including clinical and non clinical applications. He is also enthusiastic about NLP applications in oncology and how this can be used to leverage patient treatment. He is also a workshop facilitator in the European Leadership University (ELU), NL and has also been a data science educator at DataCamp. He holds a PhD from the University of Warwick, UK. in bioinformatics and ML, an MSc in statistics from Imperial College London, UK and a BSc in Statistics and Insurance Science from the University of Piraeus, GR.
Introduction to Python for Data Analysis(Bootcamp)

Julien Simon
Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years at Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups.
Hyper-productive NLP with Hugging Face Transformers(Workshop)

Daniel Whitenack, PhD
Daniel Whitenack (aka Data Dan) is a Ph.D. trained data scientist working with SIL International on NLP and speech technology for local languages in emerging markets. He has more than ten years of experience developing and deploying machine learning systems at scale. Daniel co-hosts the Practical AI podcast, has spoken at conferences around the world (Applied Machine Learning Days, O’Reilly AI, QCon AI, GopherCon, KubeCon, and more), and occasionally teaches data science/analytics at Purdue University.
Modern NLP: Pre-training, Fine-tuning, Prompt Engineering, and Human Feedback(Workshop)

Julia Lintern
Julia Lintern currently works as a Director of Data Science at Gartner. Previously, she worked as a Data Scientist for the New York Times. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods and discovered a deep appreciation for the combination of mathematics and visualizations. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.
Introduction to Machine Learning(Bootcamp)

Philip Tracton
Phil Tracton is an IC design engineer at Medtronic and an instructor at UCLA Extension. He has worked at Medtronic for over 20 years and has experience in implementing firmware, FPGAs, and custom ASICs. Many thousands of people have his work implanted in them. Most of these devices are focused on Neuromodulation. He has recently joined an internal team focused on long term research for implantable devices.
At UCLA he teaches multiple Python based courses including Learning Python and Python on the Raspberry Pi.
He is interested in low power AI on edge devices.
He will be running the Fundamentals of Python training class. This is his second time teaching at an ODSC event.
Python Fundamentals(Bootcamp)

Shawn C. Kyzer
Shawn is passionate about harnessing the power of data strategy, engineering and analytics in order to help businesses uncover new opportunities. As an innovative technologist with over 15 years experience, Shawn removes technology as a barrier, and broadens the art of the possible for business and product leaders. His holistic view of technology and emphasis on developing and motivating strong engineering talent, with a focus on delivering outcomes whilst minimising outputs, is one of the characteristics which sets him apart from the crowd.
Shawn’s deep technical knowledge includes distributed computing, cloud architecture, data science, machine learning and engineering analytics platforms. He has years of experience working as a consultant practitioner for a variety of prestigious clients ranging from secret clearance level government organizations to Fortune 500 companies.

Franz Kiraly, PhD
Franz Kiraly is the founder and a core developer of the open source framework sktime. His research is focused on software engineering for open source and data science, machine learning for structured learning tasks such as time series tasks, and robust empirical and statistical evaluation of algorithms in deployment. Franz held a faculty position at University College London 2013-2020, before he moved to industry R&D in principal data scientist roles.
sktime – Python Toolbox for Machine Learning with Time Series(Training)

Marc Rovira, PhD
Marc Rovira is a data scientist at Electrolux Group in Stockholm, with a strong focus on forecasting and time series analysis. He actively contributes to the sktime community as a council member and user representative. Prior to his industry experience, Marc completed a Ph.D. that explored the intersection of computational fluid mechanics, chemical engineering, and machine learning, with the aim of mitigating air pollution. His educational background also includes a master’s degree in aerospace engineering.
sktime – Python Toolbox for Machine Learning with Time Series(Training)

Alexandra Ebert
Alexandra Ebert is a Responsible AI, synthetic data & privacy expert and serves as Chief Trust Officer at MOSTLY AI. As a member of the company’s senior leadership team, she is engaged in public policy issues in the emerging field of synthetic data and Ethical AI and is responsible for engaging with the privacy community, with regulators, the media, and with customers. She regularly speaks at international conferences on AI, privacy, and digital banking and hosts The Data Democratization Podcast, where she discusses emerging digital policy trends as well as Responsible AI and privacy best practices with regulators, policy experts and senior executives.
Apart from her work at MOSTLY AI, she serves as the chair of the IEEE Synthetic Data IC expert group and was pleased to be invited to join the group of AI experts for the #humanAIze initiative, which aims to make AI more inclusive and accessible to everyone.
Before joining the company, she researched GDPR’s impact on the deployment of artificial intelligence in Europe and its economic, societal, and technological consequences. Besides being an advocate for privacy protection, Alexandra is deeply passionate about Ethical AI and ensuring the fair and responsible use of machine learning algorithms. She is the co-author of an ICLR paper and a popular blog series on fairness in AI and fair synthetic data, which was featured in Forbes, IEEE Spectrum, and by distinguished AI expert Andrew Ng.
When Privacy Meets AI – Your Kick-Start Guide to Machine Learning with Synthetic Data(Tutorial)

Tim Santos
Tim is leading Graphcore’s Cloud Solutions product to help AI & ML software development teams build AI products and deploy ML capabilities in production. Tim has worn many hats in his career, from being a research engineer, data scientist and leading MLOps teams. Along the way, he’s gained experience across all stages of the development lifecycle, taking AI applications from experimentation to deployment.
Generative AI in Practice: How to build your own Stable Diffusion API(Workshop)

Mark Needham
Mark Needham is an Apache Pinot advocate and developer relations engineer at StarTree. As a developer relations engineer, Mark helps users learn how to use Apache Pinot to build their real-time user-facing analytics applications. He also does developer experience, simplifying the getting started experience by making product tweaks and improvements to the documentation. Mark writes about his experiences working with Pinot at markhneedham.com. He tweets at @markhneedham.
Building a Real-time Analytics Application for a Pizza Delivery Service(Workshop)

Christian Ramirez
Christian is Machine Learning Technical Leader at Mercado Libre, the largest e-commerce/fintech company in Latin America, where he dedicates his efforts to creating tools for monitoring and quality of learning models. He is a Computer Engineer and Master in Science with a major in Astronomy from UNAM (Universidad Nacional Autonoma de Mexico). He is a “Xoogler” and has more than 15 years of experience in the field of machine learning. He has lectured in almost a dozen countries.
Introduction to Topological Data Analysis Workshop(Tutorial)

Dr. Phil Winder
Dr. Phil Winder is a multidisciplinary engineer and data scientist. As the CEO of Winder.AI, an AI consultancy, he provides AI, ML, Data Science, and MLOps development and consulting services to businesses of all sizes. Previous clients include the likes of Google, Microsoft, Shell, Nestle, the UK Government and many more. More information is available on the website: https://Winder.AI.
Phil is also the author of the book “Reinforcement Learning: Industrial Applications of Intelligent Agents” published by O’Reilly (https://rl-book.com) and was an early champion of MLOps. Over the past decade, he has also trained thousands of data scientists and is a celebrated global speaker on AI topics.
Phil holds a Ph.D. and M.Eng. in electronic engineering from the University of Hull and lives in Yorkshire, U.K., with his brewing equipment and family.

Dipanjan (DJ) Sarkar
Dipanjan (DJ) Sarkar is an acknowledged Data Scientist, published Author and Consultant with over nine years of industry experience in all things data. He was recognized as a Google Developer Expert in Machine Learning by Google in 2019, and a Champion Innovator in Cloud AI\ML by Google in 2022. He currently works as a Lead Data Scientist at Constructor Learning (formerly Schaffhausen Institute of Technology (SIT) Learning), Zurich.
Dipanjan has led advanced analytics initiatives working with Fortune 500 companies like Intel, Applied Materials, Red Hat / IBM. He works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. Dipanjan also works as an independent consultant, mentor and AI advisor in his spare time collaborating with multiple universities, organizations and startups across the globe. His passion includes solving challenging data problems as well as educating and helping people upskill in all things data. Find more about him at https://djsarkar.com

Daniel Lenton, PhD
Daniel Lenton is the creator of Ivy, which is an open-source framework with an ambitious mission to unify all other ML frameworks. Prior to starting Ivy, Daniel was a PhD student at Imperial College London, where he published research in the areas of machine learning, robotics and computer vision.
Unifying ML With One Line of Code(Tutorial)

Moez Ali
Innovator, Technologist, and a Data Scientist turned Product Manager with proven track record of building and scaling data products, platforms, and communities. Experienced in building and leading teams of data scientists, data engineers, and product managers. Strongly opinionated tech visionary and a thought partner to C-level leadership.
Moez Ali is an inventor and creator of PyCaret. PyCaret is an open-source, low-code, machine learning software. Ranked in top 1%, 8M+ downloads, 7K+ GitHub stars, 100+ contributors, and 1000+ citations.
Globally recognized personality for open-source work on PyCaret. Keynote speaker and top ten most-read writer in the field of artificial intelligence. Teaching AI and ML courses at Cornell, NY and Queens University, CA. Currently building world’s first hyper-focused Data and ML Platform.
Automate Machine Learning Workflows with PyCaret 3.0(Workshop)
Past Workshop/Training Sessions
Introduction to Data Analysis Using Pandas
sktime – Python Toolbox for Machine Learning with Time Series
Generative AI
Autoencoders – a Magical Approach to Unsupervised Machine Learning
Exploiting GNNs for Business Recommendation on Yelp Data
Generative AI in Practice: How to build your own Stable Diffusion API
Data Validation at Scale – Detecting and Responding to Data Misbehavior
Modern NLP: Pre-training, Fine-tuning, Prompt Engineering, and Human Feedback
Hyper-productive NLP with Hugging Face Transformers
AI-Powered Algorithmic Trading with Python
Turbocharge your Data Analytics Plane with AI
Learn how to Efficiently Build and Operationalize Time Series Models in 2023
Space Science with Python – Enabling Citizen Scientists
Building a Real-time Analytics Application for a Pizza Delivery Service
Automate Machine Learning Workflows with PyCaret 3.0
Feature Engineering With Signal Types
Introduction to Interpretability in ML (XAI)
Want End-to-End MLOps? Delta & Databricks Make This A Reality!
ODSC Newsletter
Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!
Half-Day Training | Deep Learning | Intermediate-Advanced
In this session, participants will be introduced to recent advances in audio-visual speech enhancement and separation, which has a variety of different applications…more details
Prof. Zheng-Hua Tan is a Professor of Machine Learning and Speech Processing, a Co-Head of the Centre for Acoustic Signal Processing Research (CASPR), and Machine Learning Research Group Leader in the Department of Electronic Systems at Aalborg University, Denmark. Prof. Zheng-Hua Tan was a Visiting Scientist/Professor at the Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, USA, an Associate Professor in the Department of Electronic Engineering at Shanghai Jiao Tong University, China, and a postdoctoral fellow at AI Spoken Language Lab, in the Department of Computer Science at KAIST, Korea. He received the B.S. and M.S. degrees in electrical engineering from Hunan University, China, in 1990 and 1996, respectively, and the Ph.D. degree in electronic engineering from Shanghai Jiao Tong University, China, in 1999. His research interests include machine learning, deep learning, pattern recognition, speech and speaker recognition, noise-robust speech processing, multimodal signal processing, and social robotics. He has over 200 publications. He edited the book Automatic Speech Recognition on Mobile Devices and over Communication Networks (Springer, 2008). He is the elected Chair of the IEEE Machine Learning for Signal Processing Technical Committee. He is an Associate Editor for the IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING. He has served as an Editorial Board Member/Associate Editor for several journals including Computer Speech and Language, and Digital Signal Processing. He was a Lead Guest Editor of the IEEE Journal of STSP and a Guest Editor of several journals including Neurocomputing. He was the General Chair of IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP 2018), Aalborg, Denmark, and was a Program Co-Chair for IEEE Workshop on Spoken Language Technology (SLT 2016), San Diego, California, USA. He has served as a Chair, Program Co-chair, Area and Session Chair, and Tutorial Speaker of many international conferences. He is a Senior Member of the IEEE.
Daniel Michelsanti is an Industrial Postdoctoral Researcher at Demant and Aalborg University. He has a PhD in Electrical and Electronic Engineering obtained at Aalborg University. Currently, he is investigating cutting-edge technologies for next-generation hearing assistive devices, with the goal of improving the life quality of people with hearing loss.
Full-Day Training | Machine Learning | Beginner-Intermediate
This session is a hands-on introduction to Machine Learning in Python with scikit-learn. You will learn to build and evaluate predictive models on tabular data using the main tools of the Python data-science stack (Jupyter, numpy, pandas, matplotlib and scikit-learn)…more details
Olivier Grisel is a machine learning engineer at Inria. He is a member of the team of maintainers of the scikit-learn project. Scikit-learn is an Open Source machine learning library written in Python. His work is supported by the Fondation Inria and its partners
Full-Day Training | NLP | Deep Learning | Intermediate-Advanced
Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any datascientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive hands-on examples to master state-of-the-art tools, techniques and methodologies for actually applying NLP to solve real- world problems. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models…more details
Anuj Gupta is a head the Machine Learning and Data Science teams at Vahan. Prior to this, he was heading ML efforts for Intuit, Huawei Technologies, Freshworks, Chennai and Airwoot, Delhi. He did his masters in theoretical computer science from IIIT Hyderabad and he dropped out of his Phd from IIT Delhi to work with startups.
He is a regular speaker at ML conferences like Pydata, Nvidia forums, Fifth Elephant, Anthill. He has also conducted a bunch of workshop attended by machine learning practitioners. He is also the co-organizer for one of the early Deep Learning meetups in Bangalore. He is also Editor of “”Anthill-2018″” – deep learning focused conference by HasGeek.
Half-Day Training | Machine Learning | Beginner
The objective of the session is to provide some basic understanding of Python as a language to be used for data processing. Python syntax is very readable and easy to work with, and its rich ecosystem of libraries makes it one of the most popular programming languages in the World.
We will see some common tools and characteristics of Python that are basic to analyse data, like how to import data from files and to generate results in multiple formats. We will also see some ways to speed-up the processing of data.
This workshop is aimed at people with little to no knowledge of Python, though some programming knowledge is required, even if it’s in a different language…more details
Jaime Buelta has been a professional programmer since 2002 and a full-time Python developer since 2010. He has developed software for a variety of fields, focusing, in the last 10 years, on developing web services in Python in the gaming and finance industries. He is a strong proponent of automating everything to make computers do most of the heavy lifting, so humans can focus on the important stuff. He published his first book, “Python Automation Cookbook”, in 2018 (now updated recently with an extended second edition), followed by “Hands-On Docker for Microservices with Python” the following year. He is currently working as Software Architect in Double Yard in Dublin, Ireland, and is a regular speaker at PyCon Ireland.
Half-Day Training | Quant Finance | Machine Learning | Intermediate
The rapid progress in machine learning (ML) and the massive increase in data availability has enabled novel approaches to quantitative investment and increased the demand for the application of data science to develop discretionary and automated trading strategies.
This workshop covers popular ML use cases for the investment industry. In particular, it focuses on how ML fits into the workflow of developing a trading strategy, from the engineering of financial features to the development of an ML model that generates tradable signals, the backtesting of a trading strategy that acts on these signals and the evaluation of its performance.
We’ll use common Python data science and ML libraries as well as Zipline, Pyfolio, and Alphalens. The code examples will be presented using jupyter notebooks and are based on the second edition of my book ‘Machine Learning for Algorithmic Trading’…more details
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.
Half-Day Training | Big Data | Beginner
This tutorial will introduce you to the wonderful world of Bayesian data science through the lens of probabilistic programming in Python. In the first half of the tutorial, we will introduce the key concepts of probability distributions via hacker statistics, hands-on simulation, and telling stories of the data-generation processes. We will also cover the basics of joint and conditional probability, Bayes’ rule, and Bayesian inference, all through hands-on coding and real-world examples. In the second half of the tutorial, we will use a series of models to build your familiarity with PyMC3, showcasing how to perform the foundational inference tasks of parameter estimation, group comparison (for example, A/B tests and hypothesis testing), and arbitrary curve regression…more details
Workshop | Big Data | All Levels
In many data science ecosystems data frame is a pivotal object. It is not only very useful conceptually, but also ensures that data transformation operations can be performed efficiently. Therefore packages like data.table in R or pandas in Python are star players.
With the Julia language the situation is different because it gives you the speed out of the box. Therefore the DataFrames.jl package is designed to be a sidekick that conveniently supports your core data analysis pipeline. It has a more focused functionality than e.g. pandas, but at the same time it seamlessly integrates with the whole Julia data science ecosystem.
During this workshop, using hands-on examples, I will discuss the design principles behind DataFrames.jl and walk you through key functionalities provided by this package. All presented materials will be made available before the workshop in a blog post on https://bkamins.github.io/…more details
Bogumił Kamiński is Head of Decision Analysis and Support Unit at Warsaw School of Economics, Poland, and Adjunct Professor at Data Science Laboratory, Ryerson University, Canada. His research interests are techniques of large scale mathematical modelling of complex systems combining simulation, optimization, and machine learning. A particular area of his expertise are agent based simulation and modeling and analysis of complex networks.
Bogumił is one of the core developers of DataFrames.jl package for data wrangling in the Julia language. He is also a top answerer for the [julia] tag on StackOverflow and regularly discusses a wide range of data science related topics on his blog https://bkamins.github.io/.
Tutorial | Machine Learning | Intermediate-Advanced
Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, YAGO, Wikidata of Google Knowledge Graph. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue...more details
Daria Stepanova is a lead research scientist at Bosch Center for Artificial Intelligence. Her research interests include knowledge representation and reasoning, machine learning and neuro-symbolic AI. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on semantic data. Daria got her PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.
Tutorials | Machine Learning | Intermediate
Automated machine learning is the science of building machine learning models in a data-driven, efficient, and objective way. It replaces manual trial-and-error with automated, guided processes. In this tutorial, we will guide you through the current state of the art in hyperparameter optimization, pipeline construction, and neural architecture search. We will discuss model-free blackbox optimization methods, Bayesian optimization, as well as evolutionary and other techniques. We will also pay attention to meta-learning, i.e. learning how to build machine learning models based on prior experience. Moreover, we will give practical guidance on how to do meta-learning with OpenML, an online platform for sharing and reusing machine learning experiments, and how to perform automated pipeline construction with GAMA, a novel, research-oriented AutoML tool in Python…more details
Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.
Pieter Gijsbers is a Ph.D. student at the Eindhoven University of Technology. His areas of interest are automated machine learning and meta-learning. He is the main author of GAMA, a research-focused open-source AutoML tool. While GAMA is easy to use for end-users, it also allows researchers to try different search techniques and visualize the optimization process. He is a co-author of the Open Source AutoML Benchmark. He is also part of the team working on the openml-python package, providing an easy to use Python interface to OpenML.
Workshop | Deep Learning | Intermediate-Advanced
CNNs, specialized neural networks for Computer Vision tasks, are used in sensitive contexts and exposed in the wild. While extremely accurate, they are also sensitive to imperceptible perturbations that can’t be detected by human eyes. For this reason, they have been targeted by hackers which implemented AI-based techniques for their malicious purposes. During this workshop we are going to learn some synthetic attacking techniques and a defence strategy to mitigate the effect of such attacks and make neural networks more robust to them, while at the same time keeping minimal impact on the accuracy of the model and implementation costs. We would also try to understand if Transformers applied to Computer Vision tasks are immune to Adversarial Attacks…more details
Guglielmo is a Biomedical Engineer with an extensive background in Software Engineering and Data Science applied to different contexts, such as Biotech Manufacturing, Healthcare and DevOps, just to mention the latest, and a lifelong learner. As part of the Manufacturing IT Advanced Mathematics and Modelling Data Science Team he is currently busy unlocking business value through Deep Learning projects, mostly in Computer Vision (not restricted to this field by the way).
He has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference.
He is also an international speaker and author of the following book: Hands-on Deep Learning with Apache Spark @Packt https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-apache-spark
Workshop | Deep Learning | Intermediate
Learn the basics of building a PyTorch model using a structured, incremental and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more.
The main goal of this session is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and “torch” it! At the end of it, you should be able to understand PyTorch’s key components and how to assemble them together into a working model.
We will use Google Colab and work our way together into building a complete model in PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object oriented programming…more details
Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping more than 150 students advance their careers.
He writes regularly for Towards Data Science. His blog post “Understanding PyTorch with an example: a step-by-step tutorial” reached more than 220,000 views since it was published.
The positive feedback from the readers motivated him to write the book Deep Learning with PyTorch Step-by-Step, which covers a broader range of topics.
Daniel is also the main contributor of two python packages: HandySpark and DeepReplay.
His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail and mobility.
Workshop | Machine Learning | Beginner-Intermediate
In this session, we will work through the basics of solving a classification-based machine learning problem using python and scikit-learn, and do a comparative study of two popular algorithms…more details
Yamini Rao is a Developer Advocate for IBM. She complies various developer scenarios and training material, including demos, writing blog posts, creating audio-visual artefacts and giving hands-on workshops and training sessions based on IBM Cloud technologies. She works with various developer communities across the UK. As a developer advocate, Yamini is involved in public speaking engagements at conferences and meetups and also organises some of these events as part of the role. The other part of Advocacy involves closing the loop, to collect feedback from the developer community about how they are using the technology and channel this back to the engineering and product management teams. She has a background in computer science and has previously worked as an Implementation Engineer for various IBM Analytical tools.
Tutorial | NLP | Deep Learning | Beginner-Intermediate
Have you wondered what is the technology behind the GPT models? In this talk, we are going to discuss the Transformer neural networks, introduced in 2017…more details
A former theoretical physicist turned machine learning engineer, Olga is now building a smart data annotation platform at Scaleway as a technical product manager. On the community side, she enjoys blogging about the latest advancements in AI both in and out of working hours. Some of her writing can be seen on medium.com/@olgapetrova_92798.
Workshop | Quant Finance | Intermediate-Advanced
This target of this workshop is twofold. On one hand, it is familiarizing attendees with mechanics of reinforcement learning (RL) applied to financial environments. On the other side, it aims to uncover key differences between popular RL applications (as playing video games) and financial ones, ignoring which inevitably will lead to losses of time and capital. With such insights and code boilerplates, attendees will be able to avoid harsh mistakes and implement environment-driven strategies faster…more details
Alex Honchar is a tech entrepreneur and educator. Currently, he is co-founder and ML director at Neurons Lab – a consulting firm specializing in healthcare, finance, and IoT. Also, he writes a popular blog on Medium about machine learning applications and leadership. Previously he worked as an independent consultant with SMBs and startups on rapid go-to-market ML solutions and taught machine learning courses at the University of Verona and Ukrainian Catholic University.
Workshop | Responsible AI | Beginner
This workshop offers a gentle introduction to reproducible and elegantly formatted document generation with R Markdown. R Markdown presents a framework for reproducible workflows. It allows you to use multiple languages including R, Python, and SQL and helps you automate the production of HTML or PDF reports by relying on the power of Pandoc together with Lua-filters.
Participants will learn how to implement literate programming practices to make their workflows fully reproducible and produce automated reports. We will first cover the basics of narrative text and code integration. Then, we focus on working on a template that is fully optimized for two different output formats, HTML and PDF. While in the stage of explorative data analysis and with an eye on content only, our template allows you to produce beautiful HTML reports of your analyses, optimized for the interactive exploration of your data. While in the stage of dissemination and with an eye on the presentation of results, our template allows you to produce beautifully typeset PDF reports, ready to be circulated or published any time…more details
Julia Schulte-Cloos is a Marie Skłodowska-Curie funded LMU Research Fellow at the Geschwister Scholl Institute of Political Science at LMU Munich. Her main research interests lie in comparative politics, political behavior, research methodology, and reproducibility. Julia Schulte-Cloos has earned her PhD from the European University Institute.
Workshop | Machine Learning | All Levels
By completing this workshop, you will develop an understanding of the different freely available RS data sources out there and open-source software tools that can be used for analysing these...more details
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year’s experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing.
Tutorial | Quant Finance | Intermediate
This tutorial explores machine learning applications in economics and finance using TensorFlow 2. It starts by examining how TensorFlow and machine learning can be used to solve empirical and theoretical models in economics…more details
Isaiah Hull is a senior economist in the research division of Sweden’s Central Bank (Sveriges Riksbank). He holds a PhD in economics from Boston College and conducts research on computational economics, machine learning, and quantum computing. He is also the instructor for DataCamp’s “Introduction to TensorFlow in Python” course and the author of “Machine Learning for Economics in Finance in TensorFlow 2.”
Workshop | MLOps | Intermediate-Advanced
Airflow is a leading open-source workflow orchestrator that offers a very wide range of possibilities. It can be integrated with Kubernetes using the KubernetesPodOperator to create pipelines that are extremely customizable. This is what we use to preprocess data and train ML models for PowerOP, Dataswati’s SAAS for optimizing food industry production lines…more details
Luis spent his education and early career in the realm of Transport (Civil Engineering Diploma, MSc Transport from Imperial College London, French Civil Aviation Authority) but he took the train of data science and machine learning in 2014 with Kaggle and Coursera as teachers. He spent about 3 years as a Data Scientist at Quantmetry, a Data Science and AI Consultancy based in Paris where he helped several big companies in multiple sectors develop solutions involving Natural Language Processing, Geospatial data, and Time series. He has recently joined Dataswati to take the lead of the Data Science team. At Dataswati, they develop a SAAS called PowerOP that offers agro-industrial companies easy integration and visualization of their production data as well as explainable and actionable AI services like quality analysis, smart alerting and recipe or settings recommendation.
Workshop | Big Data
By completing this workshop, you will develop an understanding of various vectors of AI risks to companies and be able to improve the governance inside your organization. Additionally, you will get hands-on experience on the problem of biased data and simulate an adversarial attack on a neural network model…more details
Lukas Csoka, working as Head Big Data Foundations in Swiss Re, one of the biggest global reinsurers, combines data science, consultative thinking and business partnering as the main skills used daily in his job to push design and develop new AI products. His MS in software engineering from Slovak University of Technology and MBA in global management from City University of Seattle are supporting him during this journey. His projects include innovative solutions for farmers using satellites, the application of predictive methods to manage costs inside Swiss Re or the analysis of thousands of client meetings from text and the provision of recommendations to Swiss Re’s traders and many others.
Workshop | Machine Learning | Responsible AI | Beginner
In the days where we have autonomous cars, drones, and automated medical diagnostics, we want to learn more about how to interpret the decisions made by the machine learning models. Having such information we are able to debug the models and retrain it in the most efficient way.
This talk is dedicated to managers, developers and data scientists that want to learn how to interpret the decisions made by machine learning models. We explain the difference between white and black box models, the taxonomy of explainable models and approaches to XAI. Knowing XAI methods is especially useful in any regulated company.
We go through the basic methods like the regression methods, decision trees, ensemble methods, and end with more complex methods based on neural networks. In each example, we use a different data set for each example. Finally, we show how to use model agnostic methods to interpret it and the complexity of the interpretability of many neural networks…more details
Karol Przystalski obtained a PhD degree in Computer Science in 2015 at the Jagiellonian University in Cracow. He is the CTO and founder of Codete where he’s leading and mentoring teams as they work with Fortune 500 companies on data science projects. He also built a research lab for machine learning methods and big data solutions at Codete. Karol gives speeches and trainings in data science with a focus on applied machine learning in German, Polish, and English.
Workshop | MLOps
Ivana is a data scientist, passionate about machine learning and artificial intelligence. As part of the Data Science and Strategy competence center at element61, she helps organizations build and grow business with data. Ivana is an engineering professional with a Master’s degree in Artificial Intelligence from KU Leuven.
Workshop | Machine Learning | Beginner
General Data Protection Regulation (GDPR) is now in place. Are you ready to explain your models? This is a hands-on tutorial for beginners. I will demonstrate the use of open-source H2O platform (https://www.h2o.ai/products/h2o/) with both Python and R for automatic and explainable machine learning. Participants will be able to follow and build regression and classification models quickly with H2O AutoML. They will then be able to explain the model outcomes with various methods...more details
Jo-fai (or Joe) has multiple roles (data scientist / evangelist / community manager) at H2O.ai. Since joining the company in 2016, Joe has delivered H2O talks/workshops in 40+ cities around Europe, US, and Asia. Nowadays, he is best known as the H2O #360Selfie guy. He is also the co-organiser of H2O’s EMEA meetup groups including London Artificial Intelligence & Deep Learning – one of the biggest data science communities in the world with more than 11,000 members (https://www.meetup.com/London-Artificial-Intelligence-Deep-Learning/).
Workshop | MLOps | NLP | Intermediate
In this workshop we will explore the concept of MLOps Orchestration and how it can simplify the process of getting data science to production in any environment (multi-cloud, on-prem, hybrid), from the step of data collection and preparation (across real-time / streaming, historic, structured, or unstructured data), through automated model training to model deployment and monitoring. We will demonstrate how to drastically cut down the time and efforts needed to get data science to production. We’ll show how to map a business problem into an automated ML production pipeline and identify the right tools for the job, and ultimately how to run Al models in production at scale to accelerate business value with AI – all using open source technologies. The session will include a live demo and real customer case studies across use cases such as fraud prevention, real-time recommendation engines and NLP…more details
Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in AI, cloud, data and networking to leading startups and enterprises since the late 1990s. As the Co-Founder and CTO of Iguazio, Yaron drives the strategy for the company’s MLOps platform and led the shift towards the production-first approach to data science and catering to real-time AI use cases. He also initiated and built Nuclio, a leading open source serverless framework with over 4,000 Github stars and MLRun, a cutting-edge open source MLOps orchestration framework. Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA – NASDAQ: NVDA), where he led technology innovation, software development and solution integrations. He also served as the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO and networking company which floated on the NYSE in 2007 and was later acquired by Mellanox (NASDAQ:MLNX). Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He sits on the Data Science Committee of the AI Infrastructure Alliance (AIIA), of which Iguazio is a founding member. He is co-authoring a book on Implementing MLOps in the Enterprise for O’Reilly. Yaron presents at major industry events worldwide and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.
Workshop | MLOps | Intermediate
This session will explore and demonstrate how DataRobot’s MLOps can speed up deployment, monitor drift and accuracy, ensure governance and ongoing model lifecycle management, including how to do automation retraining and have challenger models in production…more details
Asger holds a master of Computer Science and has for many years been leading software development teams as the CTO of several startup companies. Following that he has been helping many of the biggest enterprises around the world becoming successful with monitoring critical application services in production, and today at DataRobot he is focused on bringing some of the best practices from the DevOps world into the new growing field of MLOps, where some of the principles can be reused but where there is also a great need to have AI and Machine Learning specific capabilities to be able to scale in a standardised and governed way.
Qian Zhao is a London-based data scientist at DataRobot who helps fintech, banking, and healthcare customers accelerate their machine learning capabilities by using the DataRobot platform. Before he joined DataRobot, Qian was a data science manager at PwC and Deloitte, where he led a team of data scientists, delivering innovative and cutting-edge AI and machine learning solutions to enterprise businesses. Qian received his Ph.D. in Neuroscience from UCL in 2012.
Pavel Ustinov is a Lead AI Engineer at DataRobot, where he applies his scientific background and more than 15 years’ experience in software engineering to successfully deliver end-to-end Augmented Intelligence solutions driven by the DataRobot platform. He takes care of EMEA region customers. Pavel began his career as a researcher in the nonlinear dynamics of energy conversion systems before moving to the software engineering and machine learning industry. Prior to joining DataRobot, he also spent time at Anaplan, Société Générale CIB, BNP Paribas Securities Services, and ML/DL startups holding senior/leading technical positions and delivering large scale analytical projects. Pavel received his PhD from Orel State Technical University (Orel, Russia) in Automation and Control of Industrial Systems and PhD from University of Reims Champagne-Ardenne (Reims, France) in Computer Science, Automation and Signal Processing. When not working out over technology he enjoys a high altitude mountaineering.
Workshop | Life Sciences & Pharma | All Levels
A real-world scenario of applying ML in genomics will be discussed, where we shall build a predictor of a special type of DNA structures out of DNA sequence information alone…more details
Alex is a principal investigator at the University of Oxford, leading a group focused on integrative computational biology and machine learning in the MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine. In past, he did his undergraduate studies in pharmaceutical sciences with a research focus on quantum/structural chemistry and NMR spectroscopy. He next moved to the University of Cambridge, first obtaining an MPhil degree in computational biology (Department of Applied Mathematics and Theoretical Physics), followed by a PhD in theoretical chemical biology (Department of Chemistry). He then became an interdisciplinary research fellow in computational genomics and epigenetics (Department of Chemistry and Cancer Research UK Cambridge Institute), before joining the University of Oxford. His research aims at combining machine learning, computational biology, computational chemistry, data from experimental genomics and biophysical techniques to reach a new level of precision in biology at both genome and proteome levels.
Workshop | Machine Learning | All Levels
This will be a 90 minute workshop that will walk through how to set up, run and deploy a federated learning project from scratch…more details
At Scaleout, we are solving the data access challenge in AI. We are developing a world-leading solution for federated learning. In federated learning, you distribute the training of machine learning models to the data. You avoid collecting all data in one place.
Daniel is the CEO and co-founder of Scaleout and has a long background as an entrepreneur and leader in deep tech companies. He co-founded Scandinavia’s first personal DNA-testing company in 2008, was CTO at a multinational growing medtech company for 7 years, and then co-founded the first international accelerator for blockchain startups. As CTO and CEO, he has many years of experience in leading deep tech projects and taking them to market.
Workshop | Responsible Ai | Machine Learning | Intermediate-Advanced
Recently, academics as well as policy makers have written many papers, on responsible data science / AI. Moreover, many open-source packages for bias dashboards or tools for `fairness’ have been proposed. This session aims to provide attendees a broad overview as well as the specific technical background to use the available ` fairness’ tools. In addition, a governance framework describing the precise responsibilities of data scientists will be discussed…more details
Ramon van den Akker works as a data scientist at the AI Center of Excellence and the Risk Modelling departments of de Volksbank, a Dutch retail bank located in Utrecht. He also works, as an associate professor, at the econometrics group of Tilburg University. His research interests cover various fields in data science, machine learning, econometrics and statistics and his research findings have been published in leading journal in econometrics and statistics. In his work at de Volksbank, Ramon mainly works on data science projects and the exploration of new opportunities, but also on governance aspects like frameworks for responsible AI & data science and the use of privacy-preserving data analytics.
Daan Knoope works as an AI Engineer at de Volksbank, the Dutch parent company of several banks and mortgage providers. He has a background in Computer Science (MSc) and has specialized in Algorithmic Data Analysis. During his studies, he researched the application of Dynamic Bayesian Networks on practical use cases to help further the development of explainable AI. Currently, he is focusing on developing AI-models for the bank as well as providing fellow AI Engineers the tools they need to efficiently explore data and build production-ready models
Joris Krijger (1991) works as an Ethics & AI specialist at the Dutch bank de Volksbank while also holding a PhD position at the Erasmus University Rotterdam on that topic. He has a background in Economic Psychology (MA), Philosophy (MA) and Film and Literary Studies (BA) and studied in Glasgow, Buenos Aires and Leiden, where he was awarded a national thesis prize in 2017 by the Royal Dutch Society of the Sciences for his graduation thesis on technology ethics and the financial crisis of 2008. He co-founded high-tech startup Condi Food (Rabobank Wijffels Innovation Award 2014) and was involved in various biomedical initiatives related to bacteriophages. He presently works on bridging the gap between principle and practice in AI Ethics by studying the operationalization of ethical principles from an academic and practical perspective and is reviewer for the AI Ethics Journal, Subject Matter Expert for CertNexus’ ‘Certified Ethical Emerging Technologist’ and Editorial Board Member for Springer’s AI and Ethics Journal.
Workshop | Machine Learning | Quant Finance | Beginner
Unlike some of my prior presentations and tutorials that covered both statistical and neural network-based models for time series analysis, this talk will be introductory in nature and will focus on the discussion of a couple of workhorse statistical time series models that are frequently applied to solving time series forecasting problems…more details
Jeffrey Yau is currently Chief Data & A.I. Officer at Fanatics Collectibles. Most recently, he served as Global Head of Data Science, Analytics & Engineering at Amazon Music where he oversaw multiple teams who developed both insights-packed analytics and end-to-end statistical and machine learning systems. Prior to Amazon, Jeffrey worked at WalmartLabs as the VP of Data Science & Engineering where he led the team responsible for powering Walmart store mobile apps and the entire store finance system. Further, his team created end-to-end machine learning systems for key business initiatives and had a multi-billion dollar impact annually on Walmart U.S.
Over the years, he has held various senior level positions in quantitative finance at global investment management firm AllianceBernstein, consulting firm Data Science at Silicon Valley Data Science, multinational financial services company Charles Schwab Corporation, and the world’s leading professional services firm KPMG. He began his career as a tenure-track Assistant Professor of Economics at Virginia Tech, and he was an adjunct professor at UC Berkeley, Cornell, and NYU, teaching machine learning and advanced statistical modeling for finance and business.
Tutorial | Quant Finance | Beginner-Intermediate
This tutorial aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data…more details
Dr. Hao Ni is an associate professor in financial mathematics at UCL and a Turing fellow at Alan Turing Institute since September 2016. Prior to this Dr. Hao Ni was a visiting postdoctoral researcher at ICERM and Department of Applied Mathematics at Brown University from 2012/09 to 2013/05 and continued her postdoctoral research at the Oxford-Man Institute of Quantitative Finance until 2016. Dr. Hao Ni finished her D.Phil. in mathematics in 2012 under the supervision of Professor Terry Lyons at University of Oxford.
Tutorial | MLOps | All Levels
Machine learning has evolved from the experimenting stage to real-world production systems with a need for automated quality assurance and delivery, reproducibility and deployment consistency…more details
Magda is a full stack developer at Valohai and one of the organizers of the Turku.py Python meetup. She is passionate about open source, open data and MLOps.
Workshop | Deep Learning | Beginner-Intermediate
This workshop will walk you through how to convert mathematical concepts into code to build AI models. At the end of the workshop, you’ll learn how to write code from scratch to do the magic i.e. generate images and deepfake video…more details
Soon-Yau Cheong is the founder of Sooner.ai. It helps businesses to devise and implement AI strategies. Past projects include helping ARM to research using computer vision in automotive camera, and scaling up federated learning software infrastructure with Samsung Research. He has a wide interest in many AI domains including computer vision, NLP and productizing AI. Soon-Yau is also the author of “Hands-on Image Generation with TensorFlow” where he implemented many state-of-the-art models from scratch.
Workshop | Machine Learning | Intermediate-Advanced
In this workshop you will learn when and why federated learning should be used, basic algorithms for implementing it, as well as more advanced ones covering a variety of use-cases. Towards the end of the workshop participants will be offered a hands-on experience of training a federated model together…more details
Mikhail is a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. His research interests are Model fusion and federated learning; Algorithmic fairness; Applications of optimal transport in machine learning; Bayesian (nonparametric) modeling and inference. Before joining IBM, he completed Ph.D. in Statistics at the University of Michigan, where he worked with Long Nguyen. He received his bachelor’s degree in applied mathematics and physics from the Moscow Institute of Physics and Technology.
Chaitanya Kumar is a Machine Learning Engineer with the Real World AI team in the IBM Research Singapore Lab, where his work focuses on Federated Learning. Owing to his background in distributed systems, he contributes to the orchestration and deployment of Federated Learning pipelines.

Dr. Laura Wynter is the head of the RealWorld AI team at the IBM Research Singapore Lab. Laura has degrees from MIT and the Ecole des Ponts (Paris, France). Her areas of expertise involve the use of AI as well as optmization, equilibrium modeling and statistics-based methods for enabling effective real-time decision making for planning and operational problems in numerous domains. She has been named an IBM Master Inventor. Her work spans the full lifecycle of a research solution from the definition of the research problem and its characterization, to the development of effective algorithms, to collaborations with the IBM software division culminating in the creation of commercial software products from the models and algorithms developed.

Workshop | Deep Learning | NLP | All Levels
Over the past few years speech synthesis or text-to-speech (TTS) has seen rapid advances thanks to deep learning. As anyone who owns a voice assistant will know, artificial voices are becoming more and more natural and convincing. The good news is you can recreate this impressive technology yourself, using high quality open-source tools.
In this workshop, we’ll learn all about TTS and create a custom speech synthesis system from scratch. We’ll take a look at the development of TTS systems up to the present day, investigate the challenges that researchers are still grappling with, and walk through and end-to-end example of creating a deep learning-based TTS system – including data preparation, training, inference and evaluation. This workshop doesn’t require any prior knowledge of TTS or deep learning…more details
Alex Peattie is the co-founder and CTO of Peg, a technology platform helping multinational brands and agencies to find and work with top YouTubers. Peg is used by over 1500 organisations worldwide including Coca-Cola, L’Oreal and Google.
An experienced digital entrepreneur, Alex spent six years as a developer and consultant for the likes of Grubwithus, Huckberry, UNICEF and Nike, before joining coding bootcamp Makers Academy as senior coach, where he trained hundreds of junior developers. Alex was also a technical judge at this year’s TechCrunch Disrupt conference.
Workshop | NLP | Intermediate-Advanced
Transformers have taken the AI research and product community by storm. We have seen them advancing multiple fields in AI such as NLP, Computer Vision, Robotics. In this talk, I will be giving some background in Conversational AI, NLP and Transformers based Large Scale Language Models such as BERT and GPT-3…more details
Chandra Khatri is a prominent figure in technology, best known for developing state-of-the-art AI products such as the world’s first fully autonomous Conversational AI technology, the Alexa Prize (a ChatGPT-like voice experience for Alexa users 5 years before ChatGPT), ELMAR (the first Enterprise Language Model Architecture), and Truth Checker AI, the first and currently only model to detect hallucinations generated by language models such as GPT-4.
He is the co-founder of Got It AI. Under his leadership, Got It AI is pushing the boundaries of the conversational AI ecosystem and delivering the next generation of automation products. In addition to developing products, he invests in and serves on the boards of cutting-edge technology companies. Prior to Got-It AI, Chandra established or led a number of AI teams at Amazon (Alexa AI, Alexa Prize), Uber (Multimodal AI, Conversational AI), and eBay (Recommendation Systems). Furthermore, he is well-known for leading efforts at Amazon to develop the first consumer-facing, large-scale, open-domain conversational system, which is regarded as the holy grail of conversational AI and one of the unsolved challenges in artificial intelligence.
Tutorial | Machine Learning | Beginner-Intermediate
Faces are a fundamental piece of photography, and building applications around them has never been easier with open-source libraries and pre-trained models. In this tutorial, we’ll help you understand some of the computer vision and machine learning techniques behind these applications. Then, we’ll use this knowledge to develop our own prototypes to tackle tasks such as face detection (e.g. digital cameras), recognition (e.g. Facebook Photos), classification (e.g. identifying emotions), manipulation (e.g. Snapchat filters), and more…more details
Gabriel is the founder of Scalar Research, a full-service artificial intelligence & data science consulting firm. Scalar helps companies tackle complex business challenges with data-driven solutions leveraging cutting-edge machine learning and advanced analytics.
Previously, Gabriel was a B.S. & M.S. student in computer science at Stanford, where he conducted research on computer vision, deep learning, and quantum computing. He’s also spent time at Google, Facebook, startups, and investment firms.
Half-Day Training | Deep Learning | Intermediate-Advanced
In this session, participants will be introduced to recent advances in audio-visual speech enhancement and separation, which has a variety of different applications…more details
Prof. Zheng-Hua Tan is a Professor of Machine Learning and Speech Processing, a Co-Head of the Centre for Acoustic Signal Processing Research (CASPR), and Machine Learning Research Group Leader in the Department of Electronic Systems at Aalborg University, Denmark. Prof. Zheng-Hua Tan was a Visiting Scientist/Professor at the Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, USA, an Associate Professor in the Department of Electronic Engineering at Shanghai Jiao Tong University, China, and a postdoctoral fellow at AI Spoken Language Lab, in the Department of Computer Science at KAIST, Korea. He received the B.S. and M.S. degrees in electrical engineering from Hunan University, China, in 1990 and 1996, respectively, and the Ph.D. degree in electronic engineering from Shanghai Jiao Tong University, China, in 1999. His research interests include machine learning, deep learning, pattern recognition, speech and speaker recognition, noise-robust speech processing, multimodal signal processing, and social robotics. He has over 200 publications. He edited the book Automatic Speech Recognition on Mobile Devices and over Communication Networks (Springer, 2008). He is the elected Chair of the IEEE Machine Learning for Signal Processing Technical Committee. He is an Associate Editor for the IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING. He has served as an Editorial Board Member/Associate Editor for several journals including Computer Speech and Language, and Digital Signal Processing. He was a Lead Guest Editor of the IEEE Journal of STSP and a Guest Editor of several journals including Neurocomputing. He was the General Chair of IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP 2018), Aalborg, Denmark, and was a Program Co-Chair for IEEE Workshop on Spoken Language Technology (SLT 2016), San Diego, California, USA. He has served as a Chair, Program Co-chair, Area and Session Chair, and Tutorial Speaker of many international conferences. He is a Senior Member of the IEEE.
Daniel Michelsanti is an Industrial Postdoctoral Researcher at Demant and Aalborg University. He has a PhD in Electrical and Electronic Engineering obtained at Aalborg University. Currently, he is investigating cutting-edge technologies for next-generation hearing assistive devices, with the goal of improving the life quality of people with hearing loss.
Full-Day Training | Machine Learning | Beginner-Intermediate
This session is a hands-on introduction to Machine Learning in Python with scikit-learn. You will learn to build and evaluate predictive models on tabular data using the main tools of the Python data-science stack (Jupyter, numpy, pandas, matplotlib and scikit-learn)…more details
Olivier Grisel is a machine learning engineer at Inria. He is a member of the team of maintainers of the scikit-learn project. Scikit-learn is an Open Source machine learning library written in Python. His work is supported by the Fondation Inria and its partners
Full-Day Training | NLP | Deep Learning | Intermediate-Advanced
Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any datascientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive hands-on examples to master state-of-the-art tools, techniques and methodologies for actually applying NLP to solve real- world problems. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models…more details
Anuj Gupta is a head the Machine Learning and Data Science teams at Vahan. Prior to this, he was heading ML efforts for Intuit, Huawei Technologies, Freshworks, Chennai and Airwoot, Delhi. He did his masters in theoretical computer science from IIIT Hyderabad and he dropped out of his Phd from IIT Delhi to work with startups.
He is a regular speaker at ML conferences like Pydata, Nvidia forums, Fifth Elephant, Anthill. He has also conducted a bunch of workshop attended by machine learning practitioners. He is also the co-organizer for one of the early Deep Learning meetups in Bangalore. He is also Editor of “”Anthill-2018″” – deep learning focused conference by HasGeek.
Half-Day Training | Machine Learning | Beginner
The objective of the session is to provide some basic understanding of Python as a language to be used for data processing. Python syntax is very readable and easy to work with, and its rich ecosystem of libraries makes it one of the most popular programming languages in the World.
We will see some common tools and characteristics of Python that are basic to analyse data, like how to import data from files and to generate results in multiple formats. We will also see some ways to speed-up the processing of data.
This workshop is aimed at people with little to no knowledge of Python, though some programming knowledge is required, even if it’s in a different language…more details
Jaime Buelta has been a professional programmer since 2002 and a full-time Python developer since 2010. He has developed software for a variety of fields, focusing, in the last 10 years, on developing web services in Python in the gaming and finance industries. He is a strong proponent of automating everything to make computers do most of the heavy lifting, so humans can focus on the important stuff. He published his first book, “Python Automation Cookbook”, in 2018 (now updated recently with an extended second edition), followed by “Hands-On Docker for Microservices with Python” the following year. He is currently working as Software Architect in Double Yard in Dublin, Ireland, and is a regular speaker at PyCon Ireland.
Half-Day Training | Quant Finance | Machine Learning | Intermediate
The rapid progress in machine learning (ML) and the massive increase in data availability has enabled novel approaches to quantitative investment and increased the demand for the application of data science to develop discretionary and automated trading strategies.
This workshop covers popular ML use cases for the investment industry. In particular, it focuses on how ML fits into the workflow of developing a trading strategy, from the engineering of financial features to the development of an ML model that generates tradable signals, the backtesting of a trading strategy that acts on these signals and the evaluation of its performance.
We’ll use common Python data science and ML libraries as well as Zipline, Pyfolio, and Alphalens. The code examples will be presented using jupyter notebooks and are based on the second edition of my book ‘Machine Learning for Algorithmic Trading’…more details
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.
Half-Day Training | Big Data | Beginner
This tutorial will introduce you to the wonderful world of Bayesian data science through the lens of probabilistic programming in Python. In the first half of the tutorial, we will introduce the key concepts of probability distributions via hacker statistics, hands-on simulation, and telling stories of the data-generation processes. We will also cover the basics of joint and conditional probability, Bayes’ rule, and Bayesian inference, all through hands-on coding and real-world examples. In the second half of the tutorial, we will use a series of models to build your familiarity with PyMC3, showcasing how to perform the foundational inference tasks of parameter estimation, group comparison (for example, A/B tests and hypothesis testing), and arbitrary curve regression…more details
Workshop | Big Data | All Levels
In many data science ecosystems data frame is a pivotal object. It is not only very useful conceptually, but also ensures that data transformation operations can be performed efficiently. Therefore packages like data.table in R or pandas in Python are star players.
With the Julia language the situation is different because it gives you the speed out of the box. Therefore the DataFrames.jl package is designed to be a sidekick that conveniently supports your core data analysis pipeline. It has a more focused functionality than e.g. pandas, but at the same time it seamlessly integrates with the whole Julia data science ecosystem.
During this workshop, using hands-on examples, I will discuss the design principles behind DataFrames.jl and walk you through key functionalities provided by this package. All presented materials will be made available before the workshop in a blog post on https://bkamins.github.io/…more details
Bogumił Kamiński is Head of Decision Analysis and Support Unit at Warsaw School of Economics, Poland, and Adjunct Professor at Data Science Laboratory, Ryerson University, Canada. His research interests are techniques of large scale mathematical modelling of complex systems combining simulation, optimization, and machine learning. A particular area of his expertise are agent based simulation and modeling and analysis of complex networks.
Bogumił is one of the core developers of DataFrames.jl package for data wrangling in the Julia language. He is also a top answerer for the [julia] tag on StackOverflow and regularly discusses a wide range of data science related topics on his blog https://bkamins.github.io/.
Tutorial | Machine Learning | Intermediate-Advanced
Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, YAGO, Wikidata of Google Knowledge Graph. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue...more details
Daria Stepanova is a lead research scientist at Bosch Center for Artificial Intelligence. Her research interests include knowledge representation and reasoning, machine learning and neuro-symbolic AI. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on semantic data. Daria got her PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.
Tutorials | Machine Learning | Intermediate
Automated machine learning is the science of building machine learning models in a data-driven, efficient, and objective way. It replaces manual trial-and-error with automated, guided processes. In this tutorial, we will guide you through the current state of the art in hyperparameter optimization, pipeline construction, and neural architecture search. We will discuss model-free blackbox optimization methods, Bayesian optimization, as well as evolutionary and other techniques. We will also pay attention to meta-learning, i.e. learning how to build machine learning models based on prior experience. Moreover, we will give practical guidance on how to do meta-learning with OpenML, an online platform for sharing and reusing machine learning experiments, and how to perform automated pipeline construction with GAMA, a novel, research-oriented AutoML tool in Python…more details
Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.
Pieter Gijsbers is a Ph.D. student at the Eindhoven University of Technology. His areas of interest are automated machine learning and meta-learning. He is the main author of GAMA, a research-focused open-source AutoML tool. While GAMA is easy to use for end-users, it also allows researchers to try different search techniques and visualize the optimization process. He is a co-author of the Open Source AutoML Benchmark. He is also part of the team working on the openml-python package, providing an easy to use Python interface to OpenML.
Workshop | Deep Learning | Intermediate-Advanced
CNNs, specialized neural networks for Computer Vision tasks, are used in sensitive contexts and exposed in the wild. While extremely accurate, they are also sensitive to imperceptible perturbations that can’t be detected by human eyes. For this reason, they have been targeted by hackers which implemented AI-based techniques for their malicious purposes. During this workshop we are going to learn some synthetic attacking techniques and a defence strategy to mitigate the effect of such attacks and make neural networks more robust to them, while at the same time keeping minimal impact on the accuracy of the model and implementation costs. We would also try to understand if Transformers applied to Computer Vision tasks are immune to Adversarial Attacks…more details
Guglielmo is a Biomedical Engineer with an extensive background in Software Engineering and Data Science applied to different contexts, such as Biotech Manufacturing, Healthcare and DevOps, just to mention the latest, and a lifelong learner. As part of the Manufacturing IT Advanced Mathematics and Modelling Data Science Team he is currently busy unlocking business value through Deep Learning projects, mostly in Computer Vision (not restricted to this field by the way).
He has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference.
He is also an international speaker and author of the following book: Hands-on Deep Learning with Apache Spark @Packt https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-apache-spark
Workshop | Deep Learning | Intermediate
Learn the basics of building a PyTorch model using a structured, incremental and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more.
The main goal of this session is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and “torch” it! At the end of it, you should be able to understand PyTorch’s key components and how to assemble them together into a working model.
We will use Google Colab and work our way together into building a complete model in PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object oriented programming…more details
Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping more than 150 students advance their careers.
He writes regularly for Towards Data Science. His blog post “Understanding PyTorch with an example: a step-by-step tutorial” reached more than 220,000 views since it was published.
The positive feedback from the readers motivated him to write the book Deep Learning with PyTorch Step-by-Step, which covers a broader range of topics.
Daniel is also the main contributor of two python packages: HandySpark and DeepReplay.
His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail and mobility.
Workshop | Machine Learning | Beginner-Intermediate
In this session, we will work through the basics of solving a classification-based machine learning problem using python and scikit-learn, and do a comparative study of two popular algorithms…more details
Yamini Rao is a Developer Advocate for IBM. She complies various developer scenarios and training material, including demos, writing blog posts, creating audio-visual artefacts and giving hands-on workshops and training sessions based on IBM Cloud technologies. She works with various developer communities across the UK. As a developer advocate, Yamini is involved in public speaking engagements at conferences and meetups and also organises some of these events as part of the role. The other part of Advocacy involves closing the loop, to collect feedback from the developer community about how they are using the technology and channel this back to the engineering and product management teams. She has a background in computer science and has previously worked as an Implementation Engineer for various IBM Analytical tools.
Tutorial | NLP | Deep Learning | Beginner-Intermediate
Have you wondered what is the technology behind the GPT models? In this talk, we are going to discuss the Transformer neural networks, introduced in 2017…more details
A former theoretical physicist turned machine learning engineer, Olga is now building a smart data annotation platform at Scaleway as a technical product manager. On the community side, she enjoys blogging about the latest advancements in AI both in and out of working hours. Some of her writing can be seen on medium.com/@olgapetrova_92798.
Workshop | Quant Finance | Intermediate-Advanced
This target of this workshop is twofold. On one hand, it is familiarizing attendees with mechanics of reinforcement learning (RL) applied to financial environments. On the other side, it aims to uncover key differences between popular RL applications (as playing video games) and financial ones, ignoring which inevitably will lead to losses of time and capital. With such insights and code boilerplates, attendees will be able to avoid harsh mistakes and implement environment-driven strategies faster…more details
Alex Honchar is a tech entrepreneur and educator. Currently, he is co-founder and ML director at Neurons Lab – a consulting firm specializing in healthcare, finance, and IoT. Also, he writes a popular blog on Medium about machine learning applications and leadership. Previously he worked as an independent consultant with SMBs and startups on rapid go-to-market ML solutions and taught machine learning courses at the University of Verona and Ukrainian Catholic University.
Workshop | Responsible AI | Beginner
This workshop offers a gentle introduction to reproducible and elegantly formatted document generation with R Markdown. R Markdown presents a framework for reproducible workflows. It allows you to use multiple languages including R, Python, and SQL and helps you automate the production of HTML or PDF reports by relying on the power of Pandoc together with Lua-filters.
Participants will learn how to implement literate programming practices to make their workflows fully reproducible and produce automated reports. We will first cover the basics of narrative text and code integration. Then, we focus on working on a template that is fully optimized for two different output formats, HTML and PDF. While in the stage of explorative data analysis and with an eye on content only, our template allows you to produce beautiful HTML reports of your analyses, optimized for the interactive exploration of your data. While in the stage of dissemination and with an eye on the presentation of results, our template allows you to produce beautifully typeset PDF reports, ready to be circulated or published any time…more details
Julia Schulte-Cloos is a Marie Skłodowska-Curie funded LMU Research Fellow at the Geschwister Scholl Institute of Political Science at LMU Munich. Her main research interests lie in comparative politics, political behavior, research methodology, and reproducibility. Julia Schulte-Cloos has earned her PhD from the European University Institute.
Workshop | Machine Learning | All Levels
By completing this workshop, you will develop an understanding of the different freely available RS data sources out there and open-source software tools that can be used for analysing these...more details
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year’s experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing.
Tutorial | Quant Finance | Intermediate
This tutorial explores machine learning applications in economics and finance using TensorFlow 2. It starts by examining how TensorFlow and machine learning can be used to solve empirical and theoretical models in economics…more details
Isaiah Hull is a senior economist in the research division of Sweden’s Central Bank (Sveriges Riksbank). He holds a PhD in economics from Boston College and conducts research on computational economics, machine learning, and quantum computing. He is also the instructor for DataCamp’s “Introduction to TensorFlow in Python” course and the author of “Machine Learning for Economics in Finance in TensorFlow 2.”
Workshop | MLOps | Intermediate-Advanced
Airflow is a leading open-source workflow orchestrator that offers a very wide range of possibilities. It can be integrated with Kubernetes using the KubernetesPodOperator to create pipelines that are extremely customizable. This is what we use to preprocess data and train ML models for PowerOP, Dataswati’s SAAS for optimizing food industry production lines…more details
Luis spent his education and early career in the realm of Transport (Civil Engineering Diploma, MSc Transport from Imperial College London, French Civil Aviation Authority) but he took the train of data science and machine learning in 2014 with Kaggle and Coursera as teachers. He spent about 3 years as a Data Scientist at Quantmetry, a Data Science and AI Consultancy based in Paris where he helped several big companies in multiple sectors develop solutions involving Natural Language Processing, Geospatial data, and Time series. He has recently joined Dataswati to take the lead of the Data Science team. At Dataswati, they develop a SAAS called PowerOP that offers agro-industrial companies easy integration and visualization of their production data as well as explainable and actionable AI services like quality analysis, smart alerting and recipe or settings recommendation.
Workshop | Big Data
By completing this workshop, you will develop an understanding of various vectors of AI risks to companies and be able to improve the governance inside your organization. Additionally, you will get hands-on experience on the problem of biased data and simulate an adversarial attack on a neural network model…more details
Lukas Csoka, working as Head Big Data Foundations in Swiss Re, one of the biggest global reinsurers, combines data science, consultative thinking and business partnering as the main skills used daily in his job to push design and develop new AI products. His MS in software engineering from Slovak University of Technology and MBA in global management from City University of Seattle are supporting him during this journey. His projects include innovative solutions for farmers using satellites, the application of predictive methods to manage costs inside Swiss Re or the analysis of thousands of client meetings from text and the provision of recommendations to Swiss Re’s traders and many others.
Workshop | Machine Learning | Responsible AI | Beginner
In the days where we have autonomous cars, drones, and automated medical diagnostics, we want to learn more about how to interpret the decisions made by the machine learning models. Having such information we are able to debug the models and retrain it in the most efficient way.
This talk is dedicated to managers, developers and data scientists that want to learn how to interpret the decisions made by machine learning models. We explain the difference between white and black box models, the taxonomy of explainable models and approaches to XAI. Knowing XAI methods is especially useful in any regulated company.
We go through the basic methods like the regression methods, decision trees, ensemble methods, and end with more complex methods based on neural networks. In each example, we use a different data set for each example. Finally, we show how to use model agnostic methods to interpret it and the complexity of the interpretability of many neural networks…more details
Karol Przystalski obtained a PhD degree in Computer Science in 2015 at the Jagiellonian University in Cracow. He is the CTO and founder of Codete where he’s leading and mentoring teams as they work with Fortune 500 companies on data science projects. He also built a research lab for machine learning methods and big data solutions at Codete. Karol gives speeches and trainings in data science with a focus on applied machine learning in German, Polish, and English.
Workshop | MLOps
Ivana is a data scientist, passionate about machine learning and artificial intelligence. As part of the Data Science and Strategy competence center at element61, she helps organizations build and grow business with data. Ivana is an engineering professional with a Master’s degree in Artificial Intelligence from KU Leuven.
Workshop | Machine Learning | Beginner
General Data Protection Regulation (GDPR) is now in place. Are you ready to explain your models? This is a hands-on tutorial for beginners. I will demonstrate the use of open-source H2O platform (https://www.h2o.ai/products/h2o/) with both Python and R for automatic and explainable machine learning. Participants will be able to follow and build regression and classification models quickly with H2O AutoML. They will then be able to explain the model outcomes with various methods...more details
Jo-fai (or Joe) has multiple roles (data scientist / evangelist / community manager) at H2O.ai. Since joining the company in 2016, Joe has delivered H2O talks/workshops in 40+ cities around Europe, US, and Asia. Nowadays, he is best known as the H2O #360Selfie guy. He is also the co-organiser of H2O’s EMEA meetup groups including London Artificial Intelligence & Deep Learning – one of the biggest data science communities in the world with more than 11,000 members (https://www.meetup.com/London-Artificial-Intelligence-Deep-Learning/).
Workshop | MLOps | NLP | Intermediate
In this workshop we will explore the concept of MLOps Orchestration and how it can simplify the process of getting data science to production in any environment (multi-cloud, on-prem, hybrid), from the step of data collection and preparation (across real-time / streaming, historic, structured, or unstructured data), through automated model training to model deployment and monitoring. We will demonstrate how to drastically cut down the time and efforts needed to get data science to production. We’ll show how to map a business problem into an automated ML production pipeline and identify the right tools for the job, and ultimately how to run Al models in production at scale to accelerate business value with AI – all using open source technologies. The session will include a live demo and real customer case studies across use cases such as fraud prevention, real-time recommendation engines and NLP…more details
Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in AI, cloud, data and networking to leading startups and enterprises since the late 1990s. As the Co-Founder and CTO of Iguazio, Yaron drives the strategy for the company’s MLOps platform and led the shift towards the production-first approach to data science and catering to real-time AI use cases. He also initiated and built Nuclio, a leading open source serverless framework with over 4,000 Github stars and MLRun, a cutting-edge open source MLOps orchestration framework. Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA – NASDAQ: NVDA), where he led technology innovation, software development and solution integrations. He also served as the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO and networking company which floated on the NYSE in 2007 and was later acquired by Mellanox (NASDAQ:MLNX). Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He sits on the Data Science Committee of the AI Infrastructure Alliance (AIIA), of which Iguazio is a founding member. He is co-authoring a book on Implementing MLOps in the Enterprise for O’Reilly. Yaron presents at major industry events worldwide and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.
Workshop | MLOps | Intermediate
This session will explore and demonstrate how DataRobot’s MLOps can speed up deployment, monitor drift and accuracy, ensure governance and ongoing model lifecycle management, including how to do automation retraining and have challenger models in production…more details
Asger holds a master of Computer Science and has for many years been leading software development teams as the CTO of several startup companies. Following that he has been helping many of the biggest enterprises around the world becoming successful with monitoring critical application services in production, and today at DataRobot he is focused on bringing some of the best practices from the DevOps world into the new growing field of MLOps, where some of the principles can be reused but where there is also a great need to have AI and Machine Learning specific capabilities to be able to scale in a standardised and governed way.
Qian Zhao is a London-based data scientist at DataRobot who helps fintech, banking, and healthcare customers accelerate their machine learning capabilities by using the DataRobot platform. Before he joined DataRobot, Qian was a data science manager at PwC and Deloitte, where he led a team of data scientists, delivering innovative and cutting-edge AI and machine learning solutions to enterprise businesses. Qian received his Ph.D. in Neuroscience from UCL in 2012.
Pavel Ustinov is a Lead AI Engineer at DataRobot, where he applies his scientific background and more than 15 years’ experience in software engineering to successfully deliver end-to-end Augmented Intelligence solutions driven by the DataRobot platform. He takes care of EMEA region customers. Pavel began his career as a researcher in the nonlinear dynamics of energy conversion systems before moving to the software engineering and machine learning industry. Prior to joining DataRobot, he also spent time at Anaplan, Société Générale CIB, BNP Paribas Securities Services, and ML/DL startups holding senior/leading technical positions and delivering large scale analytical projects. Pavel received his PhD from Orel State Technical University (Orel, Russia) in Automation and Control of Industrial Systems and PhD from University of Reims Champagne-Ardenne (Reims, France) in Computer Science, Automation and Signal Processing. When not working out over technology he enjoys a high altitude mountaineering.
Workshop | Life Sciences & Pharma | All Levels
A real-world scenario of applying ML in genomics will be discussed, where we shall build a predictor of a special type of DNA structures out of DNA sequence information alone…more details
Alex is a principal investigator at the University of Oxford, leading a group focused on integrative computational biology and machine learning in the MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine. In past, he did his undergraduate studies in pharmaceutical sciences with a research focus on quantum/structural chemistry and NMR spectroscopy. He next moved to the University of Cambridge, first obtaining an MPhil degree in computational biology (Department of Applied Mathematics and Theoretical Physics), followed by a PhD in theoretical chemical biology (Department of Chemistry). He then became an interdisciplinary research fellow in computational genomics and epigenetics (Department of Chemistry and Cancer Research UK Cambridge Institute), before joining the University of Oxford. His research aims at combining machine learning, computational biology, computational chemistry, data from experimental genomics and biophysical techniques to reach a new level of precision in biology at both genome and proteome levels.
Workshop | Machine Learning | All Levels
This will be a 90 minute workshop that will walk through how to set up, run and deploy a federated learning project from scratch…more details
At Scaleout, we are solving the data access challenge in AI. We are developing a world-leading solution for federated learning. In federated learning, you distribute the training of machine learning models to the data. You avoid collecting all data in one place.
Daniel is the CEO and co-founder of Scaleout and has a long background as an entrepreneur and leader in deep tech companies. He co-founded Scandinavia’s first personal DNA-testing company in 2008, was CTO at a multinational growing medtech company for 7 years, and then co-founded the first international accelerator for blockchain startups. As CTO and CEO, he has many years of experience in leading deep tech projects and taking them to market.
Workshop | Responsible Ai | Machine Learning | Intermediate-Advanced
Recently, academics as well as policy makers have written many papers, on responsible data science / AI. Moreover, many open-source packages for bias dashboards or tools for `fairness’ have been proposed. This session aims to provide attendees a broad overview as well as the specific technical background to use the available ` fairness’ tools. In addition, a governance framework describing the precise responsibilities of data scientists will be discussed…more details
Ramon van den Akker works as a data scientist at the AI Center of Excellence and the Risk Modelling departments of de Volksbank, a Dutch retail bank located in Utrecht. He also works, as an associate professor, at the econometrics group of Tilburg University. His research interests cover various fields in data science, machine learning, econometrics and statistics and his research findings have been published in leading journal in econometrics and statistics. In his work at de Volksbank, Ramon mainly works on data science projects and the exploration of new opportunities, but also on governance aspects like frameworks for responsible AI & data science and the use of privacy-preserving data analytics.
Daan Knoope works as an AI Engineer at de Volksbank, the Dutch parent company of several banks and mortgage providers. He has a background in Computer Science (MSc) and has specialized in Algorithmic Data Analysis. During his studies, he researched the application of Dynamic Bayesian Networks on practical use cases to help further the development of explainable AI. Currently, he is focusing on developing AI-models for the bank as well as providing fellow AI Engineers the tools they need to efficiently explore data and build production-ready models
Joris Krijger (1991) works as an Ethics & AI specialist at the Dutch bank de Volksbank while also holding a PhD position at the Erasmus University Rotterdam on that topic. He has a background in Economic Psychology (MA), Philosophy (MA) and Film and Literary Studies (BA) and studied in Glasgow, Buenos Aires and Leiden, where he was awarded a national thesis prize in 2017 by the Royal Dutch Society of the Sciences for his graduation thesis on technology ethics and the financial crisis of 2008. He co-founded high-tech startup Condi Food (Rabobank Wijffels Innovation Award 2014) and was involved in various biomedical initiatives related to bacteriophages. He presently works on bridging the gap between principle and practice in AI Ethics by studying the operationalization of ethical principles from an academic and practical perspective and is reviewer for the AI Ethics Journal, Subject Matter Expert for CertNexus’ ‘Certified Ethical Emerging Technologist’ and Editorial Board Member for Springer’s AI and Ethics Journal.
Workshop | Machine Learning | Quant Finance | Beginner
Unlike some of my prior presentations and tutorials that covered both statistical and neural network-based models for time series analysis, this talk will be introductory in nature and will focus on the discussion of a couple of workhorse statistical time series models that are frequently applied to solving time series forecasting problems…more details
Jeffrey Yau is currently Chief Data & A.I. Officer at Fanatics Collectibles. Most recently, he served as Global Head of Data Science, Analytics & Engineering at Amazon Music where he oversaw multiple teams who developed both insights-packed analytics and end-to-end statistical and machine learning systems. Prior to Amazon, Jeffrey worked at WalmartLabs as the VP of Data Science & Engineering where he led the team responsible for powering Walmart store mobile apps and the entire store finance system. Further, his team created end-to-end machine learning systems for key business initiatives and had a multi-billion dollar impact annually on Walmart U.S.
Over the years, he has held various senior level positions in quantitative finance at global investment management firm AllianceBernstein, consulting firm Data Science at Silicon Valley Data Science, multinational financial services company Charles Schwab Corporation, and the world’s leading professional services firm KPMG. He began his career as a tenure-track Assistant Professor of Economics at Virginia Tech, and he was an adjunct professor at UC Berkeley, Cornell, and NYU, teaching machine learning and advanced statistical modeling for finance and business.
Tutorial | Quant Finance | Beginner-Intermediate
This tutorial aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data…more details
Dr. Hao Ni is an associate professor in financial mathematics at UCL and a Turing fellow at Alan Turing Institute since September 2016. Prior to this Dr. Hao Ni was a visiting postdoctoral researcher at ICERM and Department of Applied Mathematics at Brown University from 2012/09 to 2013/05 and continued her postdoctoral research at the Oxford-Man Institute of Quantitative Finance until 2016. Dr. Hao Ni finished her D.Phil. in mathematics in 2012 under the supervision of Professor Terry Lyons at University of Oxford.
Tutorial | MLOps | All Levels
Machine learning has evolved from the experimenting stage to real-world production systems with a need for automated quality assurance and delivery, reproducibility and deployment consistency…more details
Magda is a full stack developer at Valohai and one of the organizers of the Turku.py Python meetup. She is passionate about open source, open data and MLOps.
Workshop | Deep Learning | Beginner-Intermediate
This workshop will walk you through how to convert mathematical concepts into code to build AI models. At the end of the workshop, you’ll learn how to write code from scratch to do the magic i.e. generate images and deepfake video…more details
Soon-Yau Cheong is the founder of Sooner.ai. It helps businesses to devise and implement AI strategies. Past projects include helping ARM to research using computer vision in automotive camera, and scaling up federated learning software infrastructure with Samsung Research. He has a wide interest in many AI domains including computer vision, NLP and productizing AI. Soon-Yau is also the author of “Hands-on Image Generation with TensorFlow” where he implemented many state-of-the-art models from scratch.
Workshop | Machine Learning | Intermediate-Advanced
In this workshop you will learn when and why federated learning should be used, basic algorithms for implementing it, as well as more advanced ones covering a variety of use-cases. Towards the end of the workshop participants will be offered a hands-on experience of training a federated model together…more details
Mikhail is a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. His research interests are Model fusion and federated learning; Algorithmic fairness; Applications of optimal transport in machine learning; Bayesian (nonparametric) modeling and inference. Before joining IBM, he completed Ph.D. in Statistics at the University of Michigan, where he worked with Long Nguyen. He received his bachelor’s degree in applied mathematics and physics from the Moscow Institute of Physics and Technology.
Chaitanya Kumar is a Machine Learning Engineer with the Real World AI team in the IBM Research Singapore Lab, where his work focuses on Federated Learning. Owing to his background in distributed systems, he contributes to the orchestration and deployment of Federated Learning pipelines.

Dr. Laura Wynter is the head of the RealWorld AI team at the IBM Research Singapore Lab. Laura has degrees from MIT and the Ecole des Ponts (Paris, France). Her areas of expertise involve the use of AI as well as optmization, equilibrium modeling and statistics-based methods for enabling effective real-time decision making for planning and operational problems in numerous domains. She has been named an IBM Master Inventor. Her work spans the full lifecycle of a research solution from the definition of the research problem and its characterization, to the development of effective algorithms, to collaborations with the IBM software division culminating in the creation of commercial software products from the models and algorithms developed.

Workshop | Deep Learning | NLP | All Levels
Over the past few years speech synthesis or text-to-speech (TTS) has seen rapid advances thanks to deep learning. As anyone who owns a voice assistant will know, artificial voices are becoming more and more natural and convincing. The good news is you can recreate this impressive technology yourself, using high quality open-source tools.
In this workshop, we’ll learn all about TTS and create a custom speech synthesis system from scratch. We’ll take a look at the development of TTS systems up to the present day, investigate the challenges that researchers are still grappling with, and walk through and end-to-end example of creating a deep learning-based TTS system – including data preparation, training, inference and evaluation. This workshop doesn’t require any prior knowledge of TTS or deep learning…more details
Alex Peattie is the co-founder and CTO of Peg, a technology platform helping multinational brands and agencies to find and work with top YouTubers. Peg is used by over 1500 organisations worldwide including Coca-Cola, L’Oreal and Google.
An experienced digital entrepreneur, Alex spent six years as a developer and consultant for the likes of Grubwithus, Huckberry, UNICEF and Nike, before joining coding bootcamp Makers Academy as senior coach, where he trained hundreds of junior developers. Alex was also a technical judge at this year’s TechCrunch Disrupt conference.
Workshop | NLP | Intermediate-Advanced
Transformers have taken the AI research and product community by storm. We have seen them advancing multiple fields in AI such as NLP, Computer Vision, Robotics. In this talk, I will be giving some background in Conversational AI, NLP and Transformers based Large Scale Language Models such as BERT and GPT-3…more details
Chandra Khatri is a prominent figure in technology, best known for developing state-of-the-art AI products such as the world’s first fully autonomous Conversational AI technology, the Alexa Prize (a ChatGPT-like voice experience for Alexa users 5 years before ChatGPT), ELMAR (the first Enterprise Language Model Architecture), and Truth Checker AI, the first and currently only model to detect hallucinations generated by language models such as GPT-4.
He is the co-founder of Got It AI. Under his leadership, Got It AI is pushing the boundaries of the conversational AI ecosystem and delivering the next generation of automation products. In addition to developing products, he invests in and serves on the boards of cutting-edge technology companies. Prior to Got-It AI, Chandra established or led a number of AI teams at Amazon (Alexa AI, Alexa Prize), Uber (Multimodal AI, Conversational AI), and eBay (Recommendation Systems). Furthermore, he is well-known for leading efforts at Amazon to develop the first consumer-facing, large-scale, open-domain conversational system, which is regarded as the holy grail of conversational AI and one of the unsolved challenges in artificial intelligence.
Tutorial | Machine Learning | Beginner-Intermediate
Faces are a fundamental piece of photography, and building applications around them has never been easier with open-source libraries and pre-trained models. In this tutorial, we’ll help you understand some of the computer vision and machine learning techniques behind these applications. Then, we’ll use this knowledge to develop our own prototypes to tackle tasks such as face detection (e.g. digital cameras), recognition (e.g. Facebook Photos), classification (e.g. identifying emotions), manipulation (e.g. Snapchat filters), and more…more details
Gabriel is the founder of Scalar Research, a full-service artificial intelligence & data science consulting firm. Scalar helps companies tackle complex business challenges with data-driven solutions leveraging cutting-edge machine learning and advanced analytics.
Previously, Gabriel was a B.S. & M.S. student in computer science at Stanford, where he conducted research on computer vision, deep learning, and quantum computing. He’s also spent time at Google, Facebook, startups, and investment firms.
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General Pass | Get access to breakout sessions, keynotes, and virtual events. Pre-conference training and conference training and workshop sessions are not included.
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