Introduction to machine learning for bioengineers
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
- Basic concepts of machine learning
- Linear Regression
- Classification
- Resampling methods and cross-validation
- Linear Model Selection and Regularization
- Moving Beyond Linearity
- Artificial Neural Networks (Deep Learning)
- Tree-Based Methods
- Unsupervised Learning
- Basics of Reinforcement Learning
- Some state-of-the-art machine learning tools for life sciences
- Data Analysis and Machine Learning with a high-level programming language (julia)
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
Websites
Moodle Link
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
| Mo | Tu | We | Th | Fr | |
| 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 |
Légendes:
Lecture
Exercise, TP
Project, other