EE-613 / 4 credits

Teacher(s): Calinon Sylvain, Fleuret François, Odobez Jean-Marc

Language: English

Remark: Next time: Fall 2021


Frequency

Every 2 years

Summary

The objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice.

Content

Keywords

Machine learning, pattern recognition, regression.

Learning Prerequisites

Required courses

At least one prior course in probabilities, linear algebra and programming (C, Java or equivalent).

Learning Outcomes

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

  • Select appropriately in practice standard learning-based inference techniques for regression, classification and density modeling.

Assessment methods

Multiple.

 

In the programs

  • Exam form: Multiple (session free)
  • Subject examined: Machine Learning for Engineers
  • Lecture: 28 Hour(s)
  • Practical work: 28 Hour(s)
  • Exam form: Multiple (session free)
  • Subject examined: Machine Learning for Engineers
  • Lecture: 28 Hour(s)
  • Practical work: 28 Hour(s)

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