Coursebooks 2017-2018

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Machine learning

CS-433

Lecturer(s) :

Jaggi Martin Lukas
Urbanke Rüdiger

Language:

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

Notes/Handbook

github.com/epfml/ML_course

Websites

In the programs

Reference week

 MoTuWeThFr
8-9 BCH 2201 BCH 2201 
9-10   
10-11     
11-12     
12-13     
13-14     
14-15   INF119
INJ218
INM11
INM202
 
15-16    
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German