Foundations of Data Science

Weekly outline

• General

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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:

  1. Information Measures
  2. Multi-arm Bandits
  3. Detection and Estimation
     
  4. Distribution Estimation, Property Testing, and Property Estimation
  5. Exponential Families
  6. Signal Representations
     
  7. Compression and Dimensionality Reduction
  8. Information Measures and Generalization Error

  
  

  Materials

  • Lecture Notes will be posted here before the beginning of the class.

  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

  Note: Our Schedule deviates slightly from what is shown on IS-Academia.
  

  • Tuesdays:
    
    • 11:15-12:30, BC 01 (Lecture)
    • 12:30-13:15, Lunch Break
    • 13:15-14:30, BC 01 (Lecture)
    • 14:30-15:00, BC 01 (Solve HW Problem 1 together)
  • Wednesdays:
    • 13:15-15:00, GC B3 30 (Exercises) 
    • Exception: Wed, Sept 24: 13:15-15:00 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 Wednesday, November 12, 2025, 13:15-15:00
    

  • The Final Exam will take place on at some point between January 12, 2026 and January 31, 2026.
    

    
    

  
  

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    Discussion Forum External tool
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    Announcements Forum

• Week 1 (Basics of Probability)

  Sept 9: Introduction and Probability Review
  Sept 10: Exercise Session (Homework 1)
  

• Week 2 (Information Measures)

  Sept 16: Information Measures
  Sept 17: Exercise Session (Homework 2)
  

• Week 3 (Information Measures)
  Sept 23 : Information Measures
  Sept 24 : Information Measures (Lecture, exceptionally)
  

• Week 4 (Information Measures)
  Sept 30: Information Measures
  Oct 1: Exercise Session (Homework 2)

• Week 5 (Multi-Arm Bandits)
  Oct 7: Multi-Arm Bandits
  Oct 8: Exercise Session (Homework 3)
  

• Week 6 (Multi-Arm Bandits)

  Oct 14: Multi-Arm Bandits
  Oct 15: Exercise Session (Homework 3)

• Fall Break (October 20-26)

  No class, No exercise session
  

• Week 7 (Detection & Estimation)

  Oct 28: Distribution Estimation
  Oct 29: Exercise Session (Homework 4)
  

• Week 8 (Distribution Estimation)

  Nov 4: Distribution Estimation
  Nov 5: Exercise Session (Homework 4)
  

• Week 9 (Property Testing)
  Nov 11: Property Testing
  Nov 12, 13:15-15:00: Midterm Exam
  

• Week 10 (Exponential Families)

  Nov 18: Exponential Families
  Nov 19: Exercise Session (Homework 5)
  

• Week 11 (Signal Representations)

  Nov 25: Signal Representations
  Nov 26: Exercise Session (Homework 6)
  

• Week 12 (Signal Representations)

  Dec 2: Signal Representations
  Dec 3: Exercise Session (Homework 6)
  

• Week 13 (Compression)

  December 9: Compression: Dimensionality Reduction (Random Projections), then classic data compression
  December 10: Exercise Session (Homework 7)
  

• Week 14 (Information Measures and Generalization)

  December 16: Information-theoretic perspective on Generalizaton of Learning Algorithms
  December 17: Exercise Session (Homework 7)