Coursebooks

Introduction to machine learning for bioengineers

BIO-322

Lecturer(s) :

Brea Johanni Michael

Language:

English

Summary

Students understand basic concepts and methods of machine learning. They can describe them in mathematical terms and can apply them to real-world problems using the programming language R. They are familiar with some state-of-the-art machine learning tools for life sciences.

Content

Learning Prerequisites

Required courses

Algèbre linéaire, Analyse, Analyse numérique, Probabilities and statistics I & II

Learning Outcomes

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

Teaching methods

Lecture, programming labs and exercises.

Assessment methods

Resources

Bibliography

"An Introduction to Statistical Learning, with Applications in R" by
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
online available at http://faculty.marshall.usc.edu/gareth-james/ISL/

In the programs

Reference week

 MoTuWeThFr
8-9  AAC137  
9-10    
10-11     
11-12     
12-13     
13-14     
14-15     
15-16    CE1100
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