Coursebooks

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

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 No
Assistants Yes
Forum Yes
Others

TAs are available during exercise sessions.

Professor is available during the breaks of class.

Some of the exercises are run as 'integrated exercises' during the lecture

Resources

Bibliography

Pdfs of the preprint version for both books are availble online

Ressources en bibliothèque
Moodle Link

In the programs

  • Data Science, 2018-2019, 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
  • Data Science, 2018-2019, 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
  • Computer Science, 2018-2019, 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
  • Computational science and Engineering, 2018-2019, 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
  • Computational science and Engineering, 2018-2019, 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
  • Communication Systems - master program, 2018-2019, 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, 2018-2019, 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, 2018-2019, 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
  • Electrical Engineering (edoc), 2018-2019
    • 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 CE3
11-12
12-13 CE3
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22
Lecture
Exercise, TP
Project, other

legend

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