BIO-322 / 4 credits

Teacher: 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 data using a high-level programming language (julia/python/R).

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:

  • Define basic concepts of machine learning.
  • Apply machine learning tools to real-world problems.
  • Propose machine learning approaches to analyse data sets in the life sciences.

Teaching methods

Lecture, programming labs and exercises.

Assessment methods

  • Programming project during the semester
  • Written final exam

Resources

Bibliography

"An Introduction to Statistical Learning, with Applications in R" by
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
online available at https://www.statlearning.com

Ressources en bibliothèque

In the programs

  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Introduction to machine learning for bioengineers
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9  AAC137  
9-10    
10-11     
11-12     
12-13     
13-14     
14-15     
15-16    AAC137
16-17    
17-18     
18-19     
19-20     
20-21     
21-22     

Wednesday, 8h - 10h: Lecture AAC137

Friday, 15h - 17h: Exercise, TP AAC137