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.
| 24/11/2012, 10H|
|Basics, Bayesian Learning and Parameter Estimation||Slides|
| 25/11/2012, 14H30 |
|Non-parametric Learning, Clustering, Sequential Learning||Slides|
| 2/12/2012, 14H30|
|Statistical Tools for music processing, Understanding Human Auditory System||Slides1 Slides2|
| 3/12/2012, 10h|
|Sound source segregation: from the brain to the machine||Slides|
| 10/12/2012, 10h (ENST)|
- Duda, Hart and Stork, Pattern Classification, Wiley Interscience, 2000.
- C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
- E. Alpaydin, Introduction to Machine Learning, MIT Press, 2004.
- L. Wasserman, All of Statistics: A concise course in statistical inference. Springer Verlag, 2006.
The final exam and TD notes contribute to 50% of the STIM grading.