Fiches de cours

Adaptation and learning

EE-566

Enseignant(s) :

Sayed Ali H.

Langue:

English

Summary

In this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.

Content

The course covers the fundamentals of inference and learning from streaming and batch data. Students also learn about the foundations of online and batch machine learning techniques in a unified treatment. In particular, the course covers topics related to optimal inference, regularization, proximal techniques, stochastic learning, generalization theory, Bayes and naive classifiers, nearest-neighbor rules, clustering, decision trees, logistic regression, discriminant analysis, Perceptron, support vector machines, kernel methods, bagging, boosting, random forests, cross-validation, principal component analysis, and neural networks.

Learning Prerequisites

Recommended courses

Prior exposure to probability theory and linear algebra is recommended.

Dans les plans d'études

Semaine de référence

 LuMaMeJeVe
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     
En construction
 
      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