Fiches de cours

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Artificial neural networks

CS-456

Enseignant(s) :

Gerstner Wulfram

Langue:

English

Summary

Since 2010 approaches in deep learning have revolutionized fields as diverse as computer vision, machine learning, or artificial intelligence. This course gives a systematic introduction into the main models of deep artificial neural networks: Supervised Learning and Reinforcement Learning.

Content

Keywords

Deep learning, artificial neural networks, reinforcement learning, TD learning, SARSA,

Learning Prerequisites

Required courses

CS 433 Machine Learning (or equivalent)

Calculus, Linear Algebra (at the level equivalent to first 2 years of EPFL in STI or IC, such as Computer Science, Physics or Electrical Engineering)

Recommended courses

stochastic processes

optimization

Important concepts to start the course

 

Learning Outcomes

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

Transversal skills

Teaching methods

ex cathedra lectures and 2 miniprojects. Every week the ex cathedra lectures are interrupted for a short in-class exercise which is then discussed in classroom before the lecture continues.  Additional exercises are given as homework.

Expected student activities

work on miniproject

solve all exercises

attend all lectures and take notes during lecture, participate in quizzes.

If you cannot attend a lecture, then you must read the recommended book chapters

Assessment methods

written exam (70 percent) and miniproject (30 percent)

Supervision

Office hours Yes
Assistants Yes
Forum Yes
Others

TAs are available during exercise sessions.

Office hours are run in the form of one additional exercise session during the week.

Professor is available for discussions during 15 minutes after end of class.

Every week one of the exercises is run as 'integrated exercise' during the lecture

Resources

Bibliography

Pdfs of the preprint version for both books are availble online

Ressources en bibliothèque

Dans les plans d'études

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11    CE3
11-12    
12-13    CE3
13-14     
14-15     
15-16     
16-17     
17-18     
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