In charge: Michèle Sebag (CNRS, LRI - U. Paris-Sud).

1. Michele Sebag, CNRS, U. Paris-Sud (lectures)
2. Benoît Barbot, ENS-Cachan (TD/TP)

The lectures will be given in French unless attended by non French-speaking students. Slides are in English.

We don't know how to write the specifications of an algorithm able to achieve face recognition or machine translation (I mean, a competent algorithm). In such domains, an alternative is to provide examples (this is a face, this is not a face) and to let the machine itself build the algorithm able to recognize faces (called, classifier): this is machine learning.

The lecture is structured as follows:

1. An introductory course (history; goals; what is achieved and what is harder than expected; why)
2. Supervised Machine Learning
3. Unsupervised Machine Learning
4. Reinforcement Machine Learning

After Turing, machine learning was at the core of intelligent machines (one could carry through the organization of an intelligent machine with only two interfering inputs, one for pleasure or reward, and the other for pain or punishment.) But Artificial Intelligence took another direction. Why, what, etc.

Notations and formal background. Oldies but goodies: Decision trees.

1. Which results ?
2. Should I be happy with the results ?
3. Is my algorithm results better than yours ?
4. How to best tune the algorithm.

NN were at the beginning of ML, with excellent empirical results.

SVM outpassed NNs, with a strong statistical theory and efficient algorithms (nothing practical like a good theory).

Deep learning and applications.

From finding the best classifier, to finding an ensemble of them.

Finding clusters in the data; exploratory analysis. Application: you are given the catalog of a music editor. How to put some order in there ?

Modelling online data: e.g. electric consumption, job queries in a computational grid. The data are huge, their distribution is (usually) non stationary.

As usual, the problem is more than half solved if the representation/the metric is OK.

If time permits. The goal is to learn a good policy: how to (learn to) play games, to program a robotic controller.

The exam involves two written essays (un partiel et un examen final).

The questions will be in French (or in English if there are non-French speaking students). Students may answer in French or in English.

Additionally, some focused issues (with an available tutorial) will be proposed: students can volunteer to present the issue to all other students for 30mns during the lecture time.

1. basic statistics
2. computing skills

1. Probabilistic Aspects of Computer Science

1. Bishop, Christopher (2006). Pattern recognition and machine learning. Berlin: Springer. ISBN 0-387-31073-8
2. Courses on videolectures