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. 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

Mid-term and 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 http://faculty.marshall.usc.edu/gareth-james/ISL/

In the programs

  • Life Sciences Engineering, 2019-2020, Bachelor semester 5
    • Semester
      Fall
    • Exam form
      Written
    • Credits
      4
    • 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
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