Introduction to machine learning for bioengineers
Caution, these contents corresponds to the coursebooks of last year
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
- Type: optional