Fiches de cours

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Machine learning

CS-433

Enseignant(s) :

Flammarion Nicolas Henri Bernard
Jaggi Martin

Langue:

English

Summary

Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

Content

  1. Basic regression and classification concepts and methods: Linear models, overfitting, linear regression, Ridge regression, logistic regression, and k-NN.
  2. Fundamental concepts: cost-functions and optimization, cross-validation and bias-variance trade-off, curse of dimensionality.
  3. Unsupervised learning: k-Means Clustering, Gaussian mixture models and the EM algorithm.
  4. Dimensionality reduction: PCA and matrix factorization, word embeddings
  5. Advanced methods: generalized linear models, SVMs and Kernel methods, Neural networks and deep learning

Keywords

Learning Prerequisites

Required courses

 

Recommended courses

 

Important concepts to start the course

Learning Outcomes

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

Teaching methods

 

Expected student activities

Students are expected to: 

 

Assessment methods

 

Supervision

Office hours Yes
Assistants Yes
Forum Yes

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

Ressources en bibliothèque
Notes/Handbook

https://github.com/epfml/ML_course

Websites

Dans les plans d'études

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15   INF119
INF2
INJ218
INM202
INR219		 
15-16    
16-17   SG1 
17-18 SG1  
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