BIO-322 / 4 credits
Teacher: Brea Johanni Michael
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).
- Basic concepts of machine learning
- Linear Regression
- Resampling methods and cross-validation
- Linear Model Selection and Regularization
- Moving Beyond Linearity
- Artificial Neural Networks (Deep Learning)
- Tree-Based Methods
- Support Vector Machines
- Unsupervised Learning
- Some state-of-the-art machine learning tools for life sciences
- Data Analysis and Machine Learning with a high-level programming language (julia/python/R)
Algèbre linéaire, Analyse, Analyse numérique, Probabilities and statistics I & II
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.
Lecture, programming labs and exercises.
- Programming project during the semester
- Written final exam
"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