Machine Learning

Slides

• Gabriel Peyré (CNRS and ENS): Inverse Problems meets Statistical Learning.
• Alexandre Gramfort (INRIA): Introduction to standard classification models.
• Gael Varoquaux (INRIA): Scikit-Learn.

Computational resources

• Scikit-Learn: machine learning in Python.
• The numerical Tours of Signal Processing (in Matlab, Python and Julia) contains a section dedicated to convex optimization, with applications.

Bibliography

Here is a (biased) bibliography related to the lectures.

• Kevin Patrick Murphy, Machine Learning: a Probabilistic Perspective, MIT Press, 2012.
• Jerome H. Friedman, Robert Tibshirani et Trevor Hastie, The Elements of Statistical Learning, Springer, 2009.
• H. W. Engl, M. Hanke, and A. Neubauer, Regularization of Inverse Problems, Springer, 2000.