- français
- English
Fiches de cours
Learning theory
CS-526
Enseignant(s) :
Macris NicolasUrbanke Rüdiger
Langue:
English
Summary
Machine learning and data analysis are becoming increasingly central in many sciences and applications. This course concentrates on the theoretical underpinnings of machine learning.Content
- Basics : statistical learning framework, Probably Approximately Correct (PAC) learning, learning with a finite number of classes, Vapnik-Chervonenkis (VC) dimension, non-uniform learnability, complexity of learing.
- Neural Nets : representation power of neural nets, learning and stability, PAC Bayes bounds.
- Graphical model learning.
- Non-negative matrix factorization, Tensor decompositions and factorization.
- Learning mixture models.
Learning Prerequisites
Recommended courses
- Analysis I, II, III
- Linear Algebra
- Machine learning
- Probability
- Algorithms (CS-250)
Learning Outcomes
By the end of the course, the student must be able to:- Explain the framework of PAC learning
- Explain the importance basic concepts such as VC dimension and non-uniform learnability
- Describe basic facts about representation of functions by neural networks
- Describe recent results on specific topics e.g., graphical model learning, matrix and tensor factorization, learning mixture models
Teaching methods
- Lectures
- Exercises
Expected student activities
- Attend lectures
- Attend exercises sessions and do the homework
Assessment methods
Final exam and graded homeworks
Supervision
| Office hours | Yes |
| Assistants | Yes |
| Forum | Yes |
| Others | Course website |
Dans les plans d'études
- SemestrePrintemps
- Forme de l'examenEcrit
- Crédits
4 - Matière examinée
Learning theory - Cours
2 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestrePrintemps
- Forme de l'examenEcrit
- Crédits
4 - Matière examinée
Learning theory - Cours
2 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestrePrintemps
- Forme de l'examenEcrit
- Crédits
4 - Matière examinée
Learning theory - Cours
2 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestrePrintemps
- Forme de l'examenEcrit
- Crédits
4 - Matière examinée
Learning theory - Cours
2 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestrePrintemps
- Forme de l'examenEcrit
- Crédits
4 - Matière examinée
Learning theory - Cours
2 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestrePrintemps
- Forme de l'examenEcrit
- Crédits
4 - Matière examinée
Learning theory - Cours
2 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestrePrintemps
- Forme de l'examenEcrit
- Crédits
4 - Matière examinée
Learning theory - Cours
2 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
- SemestrePrintemps
- Forme de l'examenEcrit
- Crédits
4 - Matière examinée
Learning theory - Cours
2 Heure(s) hebdo x 14 semaines - Exercices
2 Heure(s) hebdo x 14 semaines
- Semestre
Semaine de référence
| Lu | Ma | Me | Je | Ve | |
|---|---|---|---|---|---|
| 8-9 | INM202 | ||||
| 9-10 | |||||
| 10-11 | |||||
| 11-12 | |||||
| 12-13 | |||||
| 13-14 | |||||
| 14-15 | |||||
| 15-16 | |||||
| 16-17 | |||||
| 17-18 | INR219 | ||||
| 18-19 | |||||
| 19-20 | |||||
| 20-21 | |||||
| 21-22 |
Cours
Exercice, TP
Projet, autre
légende
- Semestre d'automne
- Session d'hiver
- Semestre de printemps
- Session d'été
- Cours en français
- Cours en anglais
- Cours en allemand