Instructor: Arshia Cont

This course provides an introduction to pattern recognition and statistical learning for the ATIAM Masters at Ircam during the school year 2009-2010. 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; musical applications.




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 15/12/2009 at 14h30 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 22/12/2009 at 10h, lasting one hour.

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


Date Topic Material Size
09/11/2009 Introduction, Bayesian decision theory slides (~10Mb)
16/11/2009 Maximum Likelihood, Bayesian Parameter Estimation
  • Group Homework 1
slides (~6.8Mb)
30/11/2009 Bayesian Parameter Estimation, Priors and Conjugates, Kernel Densities, K-NN, Mixture Densities, Basics of EM slides (~10Mb)
01/12/2009 EM Algorithm, Clustering, Sequential Learning, HMMs and variants slides (~7.8Mb)
07/12/2009 Linear Discriminant Learning, Gradient Descent, Intro to SVM, Large Margin Classifiers slides (~4.3Mb)

Group Homeworks

Due Date Topic Set Size
30/11/2009 Maximum Likelihood on Polynomial Regression PDF (~400kb)
7/12/2009 Bayesian Parameter Estimation on Multinomial/histogram problems PDF (~650kb)
7/12/2009 EM derivation for mixtures of exponential distributions PDF (~182kb)
7/12/2009 HMM State Duration Modeling PDF (~295kb)
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