Coursebooks 2016-2017

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Pattern classification and machine learning

CS-433

Lecturer(s) :

Jaggi Martin Lukas
Urbanke Rüdiger

Language:

English

Summary

Pattern classification occupies a central role in machine learning from data. In this course, basic principles and methods underlying machine learning will be introduced. The student will learn few basic methods and their relations to each other.

Content

  1. Basic regression and classification methods: Linear regression, Ridge regression, logistic regression, and k-NN.
  2. Basic concepts: cost-functions and optimization, corss-validation and bias-variance trade-off, curse of dimensionality. 
  3. Advanced regression and classification methods: generalized linear model, SVM and Kernel methods, Gaussian processes and Bayesian methods, Neural network and deep learning, random forest and boosting.
  4. Clustering: Mixture model, k-means, Gaussian mixture model and EM algorithm.
  5. Dimensionality reduction: PCA and matrix factorization.
  6. Time-series: Bayesian network, Kalman filters and HMM, belief propagation.

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:

Transversal skills

Teaching methods

Expected student activities

Assessment methods

Resources

Bibliography

The following books will be used for further readings.

Ressources en bibliothèque
Références suggérées par la bibliothèque
Notes/Handbook

The course comes with partially-filled lecture notes which will be available to students before each lecture. These notes will NOT be complete and students are supposed to complete them during/after a lecture. This way students will be able to create their own written notes on top of the one provided to them.

Websites

In the programs

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
Under construction
 
      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