Généralités
Résumé de section
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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 introductory ML course. In particular we discuss fundamental ideas and techniques that come from probability, information theory as well as signal processing.
Content
This class presents basic concepts of Information Theory and Signal Processing and their relevance to emerging problems in Data Science and Machine Learning.
A tentative list of topics covered is:
- Information Measures
- Multi-arm Bandits
- Detection and Estimation
- Distribution Estimation, Property Testing, and Property Estimation
- Exponential Families
- Signal Representations
- Compression and Dimensionality Reduction
- Information Measures and Generalization Error
Materials
- Lecture Notes will be downloadable from here very soon!
Additional Material:- T. M. Cover and J. A. Thomas, Elements of Information Theory (Click to get access to the full PDF via the EPFL library). New York: Wiley. Second Edition, 2006.
- T. Lattimore and C. Szepesvari, Bandit Algorithms
Schedule
- Tuesdays:
- 11:15-13:00, BC 01 (Lecture)
- 13:15-15:00, BC 01 (Exercises)
- Thursdays:
- 8:15-10:00, BC 01 (Lecture)
ED Discussion Forum
- We will use the ED Discussion Forum for this class. Everyone is strongly encouraged to make the most of this!
- Ask questions!
- Answer questions!
- The class staff will check the forum on Monday afternoon and on Thursday afternoon.
SWITCHtube Channel
We will not make new video recordings this year. You can access the videos from a couple of years ago. The content is largely the same, but the order of the topics is slightly different.
Grading
- If you do not hand in your final exam your overall grade will be NA.
- Otherwise, your grade will be determined based on the following weighted average: 10% for the Homework, 30% for the Midterm Exam, 60% for the Final Exam.
- The Midterm Exam will take place on Thursday, November 12, 2026, 8:15-10:00
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The Final Exam is an Oral Exam and will take place on at some point between January 11, 2027 and January 30, 2027.
