ATIAM ML 2010

From IMTR

Instructor: Arshia Cont & Mathieu Lagrange

This course provides an introduction to pattern recognition and statistical learning for the ATIAM Masters at Ircam during the school year 2010-2011. 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.

Contents


Resources

Grading

Grades for the ATIAM Machine Learning course will be distributed evenly between two grade options in ATIAM:

1. Group Grade: A grade based on reports of a TP (Travaux Pratique) which will be held on TBD at ENST, including realization of a Matlab application following given instructions on a musical/machine learning problem.

  • 20% of this grade comes from the Group Homework assignments given throughout the course.
  • This grade will be distributed within the TSM option and constitues 30% of the overall grade (or 6 over 20).

2. A written exam on TBD, lasting one hour.

  • This grade will be distributed evenly with the final STIM grade (50%)

Lectures

Date Topic Material Size
04/11/2010 Introduction slides (6Mb)
08/11/2010 Generative vs. Discriminative Learning, Bayesian Decision Theory, Maximum Likelihood slides (14Mb)
15/11/2010 Bayesian Parameter Estimation, Kernel Densities, K-NN, EM Algorithm, Clustering, Sequential Learning slides (18Mb)
18/11/2010 ()

Group Homeworks

# Topic Set Size
1 Maximum Likelihood on Polynomial Regression PDF (~400kb)
2 Bayesian Parameter Estimation on Multinomial/histogram problems PDF (~650kb)
3 EM derivation for mixtures of exponential distributions PDF (~182kb)
4 HMM State Duration Modeling PDF (~295kb)
Personal tools