Biological data science II : machine learning

BIO-322 / 4 crédits

Enseignant: Brea Johanni Michael

Langue: Anglais

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
  • Cross-Validation and Model Selection
  • Hyper-Parameter Tuning
  • Feature Engineering
  • Gradient Descent
  • 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 (python/julia)

Learning Prerequisites

Required courses

Algèbre linéaire, Analyse, Analyse numérique, Probability and Statistics, Biological data science I: statistical learning

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

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Biological data science II : machine learning
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Type: optionnel

Semaine de référence

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