Abstract: 

This workshop aims to provide an introduction to topological data analysis (TDA), a rapidly evolving area of research that focuses on studying the shape of high-dimensional data. With the increasing availability of large and complex datasets in various domains, the need for sophisticated methods to analyze and understand these datasets has also grown. TDA strives to develop a more comprehensive understanding of data by analyzing its geometry and topology.

The workshop is designed for beginners who may have a basic understanding of mathematical concepts and are interested in learning about TDA and its applications in data science, machine learning, and numerous other fields.

Session Outline:

The primary objective of this workshop is to:

1. Familiarize the participants with the fundamental concepts of topological data analysis.
2. Provide practical examples to illustrate the use of TDA.
3. Enable participants to apply TDA techniques to their own datasets.

Topics to be covered:

Welcome and Introduction to Workshop
Basics of Topology: simplexes, complexes, and homology
Introduction to Topological Data Analysis (TDA)
The Mapper Algorithm
Persistent Homology
Vietoris-Rips Complex
Understanding shapes in high-dimensional data
TDA applications in data science
Hands-on session: Python libraries for TDA (scikit-tda, Ripser, etc.)
Use case: Analyzing real-world datasets using TDA techniques
Conclusion and Further Resources

Workshop Delivery Method:

The workshop will be delivered through a combination of lectures, hands-on sessions, and group discussions. Participants will work on practical examples using TDA algorithms in Python, enabling them to apply the concepts to real datasets. Additionally, group discussions will encourage participants to share their experiences and ask questions, fostering an engaging learning environment.

By the end of the workshop, participants will:

1. Have a comprehensive understanding of the key concepts and algorithms utilized in TDA.
2. Be able to analyze the topological properties of high-dimensional data.
3. Apply TDA techniques to real-world datasets using Python libraries.

I hope that this workshop will spark the interest of participants in the field of topological data analysis and encourage them to explore further opportunities for research, development, and applications of TDA in their respective fields.

Background Knowledge:

Basic knowledge of mathematical concepts (linear algebra, calculus) would be helpful but not mandatory. Familiarity with Python programming language is preferred.

Bio: 

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.