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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 | Intro to machine learning in music | |
| 3/12/2012, 10h Mathieu Lagrange | Sound source segregation: from the brain to the machine | |
| 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.