Foundations of Data Science

Summary

We discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introduction ML course. In particular we discuss fundamentals ideas and techniques that come from probability, information theory as well as signal processing.

Content

Keywords

Information Theory, Signal Processing, Statistical Signal Processing, Machine Learning, Data Science

Learning Prerequisites

Required courses

COM-300 Modèles stochastiques pour les communications

Recommended courses

Statistics

Important concepts to start the course

Solid understanding of linear algebra and probability as well as real and complex analysis.

Learning Outcomes

By the end of the course, the student must be able to:

• Formulate the fundamental concepts of signal processing such as basis representations and sampling
• Formulate the fundamentals concepts of information theory such as entropy and mutual information
• Analyze problems in statistical settings using fundamental bounds from information theory
• Formulate problems using robust and universal techniques

Teaching methods

Ex cathedra lectures, exercises, and small projects.

Expected student activities

Follow lectures; independent work on problems (homework and small projects).

Assessment methods

Oral final exam during the exam session.

Homework Problem Sets during the semester.

Homework 10%

Midterm 30%

Final exam 60%

Resources

Bibliography

Cover and Thomas, Elements of Information Theory (Second edition), Wiley 2006.

Ressources en bibliothèque

• Find the references at the Library

Notes/Handbook

Lectures notes will be available on the course web page

Moodle Link

• https://go.epfl.ch/COM-406