Coursebooks 2017-2018

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Artificial neural networks

CS-456

Lecturer(s) :

Gerstner Wulfram

Language:

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 Pattern Classification and 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 miniproject

Expected student activities

work on miniproject

attend all lectures

read book chapters and relevant tutorials

solve all exercises

Assessment methods

written exam (70 percent) and miniproject (3 percent)

Resources

Bibliography

Links to videos of presentations given by people in deep learning

Ressources en bibliothèque

In the programs

  • Data Science, 2017-2018, Master semester 2
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      4
    • Subject examined
      Artificial neural networks
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
  • Computer Science, 2017-2018, Master semester 2
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      4
    • Subject examined
      Artificial neural networks
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
  • Communication Systems - master program, 2017-2018, Master semester 2
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      4
    • Subject examined
      Artificial neural networks
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
  • Communication Systems - master program, 2017-2018, Master semester 4
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      4
    • Subject examined
      Artificial neural networks
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
  • Biocomputing minor, 2017-2018, Spring semester
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      4
    • Subject examined
      Artificial neural networks
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks

Reference week

MoTuWeThFr
8-9
9-10
10-11 CE1106
11-12
12-13 CE1106
13-14
14-15
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