# 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 2013-2014. 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 | |
---|---|---|

24/11/2012, 10H Arshia Cont | Basics, Bayesian Learning and Parameter Estimation | Slides |

25/11/2012, 14H30 Arshia Cont | Non-parametric Learning, Clustering, Sequential Learning | Slides |

2/12/2012, 14H30 Mathieu Lagrange | Statistical Tools for music processing, Understanding Human Auditory System | Slides1 Slides2 |

3/12/2012, 10h Mathieu Lagrange | Sound source segregation: from the brain to the machine | Slides |

10/12/2012, 10h (ENST) Arshia Cont | TD |

### 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.