CS 3750  Advanced Topics in Machine Learning (ISSP 3535)


Time:  Tuesday, Thursday 4:00pm - 5:15pm am 
Location: Sennott Square, Room 5313


Instructor:  Milos Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos_at_cs_pitt_edu
office hours: TBA


Topics and related readings.

Basics of Matrix algebra

Basics of density estimation Bayesian Belief networks

Complexity Results

Exact inference algorithms

Monte Carlo methods:

Loopy Belief propagation

Learning Belief networks from data

Basics:

EM

Component analysis

PCA and SVD

Applications of PCA:

EM algorithms for PCA

Latent Variable Models for text analysis, information retrieval and link analysis

Probabilistic component analysis

Variational methods

Introduction:

Variational ML learning for component analysis

Variational Bayesian learning:

Other variational learning papers

Laplacian Eigenmaps

Spectral clustering

Kernel methods

Support vector machines (basics):

Kernel methods (basics):

Kernel PCA:

Kernel ICA:

Various kernels

Diffusion kernels

Semi-supervised learning, label propagation

Metric learning

Gaussian processes

Videolectures:

Dirichlet processes



Last updated by
milos on 11/30/2011