# ATIAM ML 2010

### From IMTR

**Instructor:** Arshia Cont & Mathieu Lagrange

This course provides an introduction to pattern recognition and statistical learning for the ATIAM Masters at Ircam during the school year 2010-2011. The goal of the course series is to expose students to problem solving tools using machine learning techniques and intuitions behind each approach.

Topics covered include: Bayesian decision theory; parameter estimation; maximum likelihood; Bayesian parameter estimation; conjugate and non-informative priors; dimensionality and dimensionality reduction; principal component analysis; linear discriminant analysis; density estimation: parametric vs. kernel-based methods; Nearest Neighbor methods; mixture models; expectation-maximization; Sequential Learning; HMMs; Computational Auditory Analysis; Source Separation; and musical applications.

## Contents |

### Resources

- Duda, Hart and Stork,
*Pattern Classification*, Wiley Interscience, 2000. - C. Bishop,
*Pattern Recognition and Machine Learning*, Springer, 2006. - T. Hastie, R. Tibshirani, J. Friedman,
*Elements of Statistical learning*, Springer, 2001. (Also available online) - E. Alpaydin,
*Introduction to Machine Learning*, MIT Press, 2004. - L. Wasserman,
*All of Statistics: A concise course in statistical inference.*Springer Verlag, 2006.

### Grading

Grades for the ATIAM Machine Learning course will be distributed evenly between two grade options in ATIAM:

1. *Group Grade*: A grade based on reports of a TP (Travaux Pratique) which will be held on **TBD** at ENST, including realization of a Matlab application following given instructions on a musical/machine learning problem.

- 20% of this grade comes from the
*Group Homework*assignments given throughout the course. - This grade will be distributed within the TSM option and constitues 30% of the overall grade (or 6 over 20).

- 20% of this grade comes from the

2. A written exam on **TBD**, lasting one hour.

- This grade will be distributed evenly with the final STIM grade (50%)

### Lectures

Date | Topic | Material | Size |
---|---|---|---|

04/11/2010 | Introduction | slides | (6Mb) |

08/11/2010 | Generative vs. Discriminative Learning, Bayesian Decision Theory, Maximum Likelihood
- Group Homework 1
- C. Bishop's slides on Probability Distributions
| slides | (14Mb) |

15/11/2010 | Bayesian Parameter Estimation, Kernel Densities, K-NN, EM Algorithm, Clustering, Sequential Learning | slides | (18Mb) |

18/11/2010 | () |

### Group Homeworks

# | Topic | Set | Size |
---|---|---|---|

1 | Maximum Likelihood on Polynomial Regression | (~400kb) | |

2 | Bayesian Parameter Estimation on Multinomial/histogram problems | (~650kb) | |

3 | EM derivation for mixtures of exponential distributions | (~182kb) | |

4 | HMM State Duration Modeling | (~295kb) |