Machine Learning
Course description
This course is an introduction to statistical learning (Machine Learning). The general objective is exploiting databases (often large) to automatically extract prediction models (e.g. image recognition). At the end of this course, the student will be able to:
- identify the possibilities of Machine Learning
- identify standard Machine Learning techniques and tools
- train an algorithm
- identify the limits of the model obtained
Course content
Part 1 – basic tools
- Reduction dimension
- Clustering
- Multiple linear regression
- Variable selection
Part 2– advanced methods
- Non parametric methods (nearest neighbors, decision trees)
- Agregated models (random forest, bagging, boosting)
- Introduction to neural networks and deep learning
Keywords
Statistical learning, machine learning.
Bibliography
- Machine Learning: A Probabilistic Perspective, by Kevin P. Murphy, 2012
- The Elements of Statistical Learning (2nd Edition), by Trevor Hastie, Robert Tibshirani and Jerome Friedman, 2009
- An introduction to statistical learning (2nd Edition), by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, 2021
- Machine Learning in Python, by Michael Bowles, 2015
Teaching team biography
Bernard Delyon and Valérie Monbet are professors at the University of Rennes in the Institute for Research in Mathematics. Their research deal with modeling complex data and learning.