Advanced Machine Learning

Slides

• Gabriel Peyré (CNRS and ENS): Supervised Learning via Empirical Risk Minimization (+ written notes).
• Alexandre Gramfort (INRIA): Unsupervised Learning via Clustering.

Computational resources

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

Bibliography

General references on machine learning:

• 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.

Bibliography on convergence rates for ridge regression:

• Frank Bauer, Sergei Pereverzev, Lorenzo Rosasco, On Regularization Algorithms in Learning Theory.
• Gabriel Peyré Mathematical Tours of Data Sciences.