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Fiches de cours
Foundations of Data Science
COM-406
Enseignant(s) :
Urbanke RüdigerLangue:
English
Summary
Information Theory and Signal Processing are key underpinnings of Data Science. They provide frameworks for signal representation and for fundamental performance bounds.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:
- Signal Representations
- Measures of Information
- Compression and Quantization
- Sparsity
- Exponential Families, Maximum Entropy
- Detection and Estimation Theory
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 fundamental 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
Written final exam during the exam session.
Homework Problem Sets during the semester.
10% homework, 90% final exam.
Resources
Bibliography
Cover and Thomas, Elements of Information Theory (Second Edition), Wiley, 2006.
Ressources en bibliothèque
Notes/Handbook
Lectures notes
Dans les plans d'études
- SemestreAutomne
- Forme de l'examenEcrit
- Crédits
6 - Matière examinée
Foundations of Data Science - Cours
4 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestreAutomne
- Forme de l'examenEcrit
- Crédits
6 - Matière examinée
Foundations of Data Science - Cours
4 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestreAutomne
- Forme de l'examenEcrit
- Crédits
6 - Matière examinée
Foundations of Data Science - Cours
4 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestreAutomne
- Forme de l'examenEcrit
- Crédits
6 - Matière examinée
Foundations of Data Science - Cours
4 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestreAutomne
- Forme de l'examenEcrit
- Crédits
6 - Matière examinée
Foundations of Data Science - Cours
4 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestreAutomne
- Forme de l'examenEcrit
- Crédits
6 - Matière examinée
Foundations of Data Science - Cours
4 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestreAutomne
- Forme de l'examenEcrit
- Crédits
6 - Matière examinée
Foundations of Data Science - Cours
4 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestreAutomne
- Forme de l'examenEcrit
- Crédits
6 - Matière examinée
Foundations of Data Science - Cours
4 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestreAutomne
- Forme de l'examenEcrit
- Crédits
6 - Matière examinée
Foundations of Data Science - Cours
4 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestreAutomne
- Forme de l'examenEcrit
- Crédits
6 - Matière examinée
Foundations of Data Science - Cours
4 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
Semaine de référence
Lu | Ma | Me | Je | Ve | |
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10-11 | |||||
11-12 | |||||
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14-15 | |||||
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19-20 | |||||
20-21 | |||||
21-22 |
légende
- Semestre d'automne
- Session d'hiver
- Semestre de printemps
- Session d'été
- Cours en français
- Cours en anglais
- Cours en allemand