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 2012-2013. 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.
Topics | Material | |
---|---|---|
12/11/2012, 10H Arshia Cont | Basics, Bayesian Learning and Parameter Estimation | Slides |
5/12/2012, 14H30 Arshia Cont | Non-parametric Learning, Clustering, Sequential Learning | Slides |
11/12/2012, 10H Mathieu Lagrange | Intro to machine learning in music | Slides |
11/12/2012, 14h30 Mathieu Lagrange | Sound source segregation: from the brain to the machine | Slides ASA Slides BSS |
12/12/2012, 9h30 (ENST) Mathieu Lagrange | TD |
The final exam and TD notes contribute to 50% of the STIM grading.