# ATIAM Machine Learning & Music Course

**Instructors:** Arshia Cont and Mathieu Lagrange

This course provides an introduction to pattern recognition and statistical learning for the ATIAM Masters at Ircam during the school year 2011-2012. 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.

### Lectures

Topics | Material | |
---|---|---|

7/11/2011, 10H Mathieu Lagrange | Intro to machine learning in music | Slides |

8/11/2011, 10H Arshia Cont | Basics, Bayesian Learning and Parameter Estimation | Slides |

10/11/2011, 14h30 Arshia Cont | Non-parametric Learning, Clustering, Sequential Learning | Slides |

17/11/2011, 14H (ENST) Mathieu Lagrange | TD | |

1/12/2011, 10h Mathieu Lagrange | Sound source segregation: from the brain to the machine | Source Separation ASA |

### Group Homeworks

### 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

The final exam and TD notes contribute to 50% of the STIM grading.