Machine Learning Course 2015 Spring

Course Description

This course provides an advanced introduction to machine learning.

Topics include:

Introduction
Basic algorithm review
Support vector machine
Conditional random fields
Probabilistic topic model
Bayesian Non-parametric learning
Sparse learning
Deep learning

Course Material

Course Materials

There is no required text for this course. Notes will be posted periodically on the course web site.The following books are recommended as optional reading:

Daphne Koller and Nir Friedman. Probabilistic Graphical Models. MIT Press, 2009.
Christopher M. Bishop. Pattern Recognition and Machine Learning, Springer, 2007.
John Hopcroft. Computer Science Theory for the Information Age. 2011.
Michael I. Jordan. An Introduction to Probabilistic Graphical Models. University of California, Berkeley. June 30, 2003.
Martin J. Wainwright and Michael I. Jordan. Graphical Models, Exponential Families, and Variational Inference, Foundations and Trends in Machine Learning, V1 (1-2), 2008.
Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. Springer, 2003.
Yoshua Bengio. Learning Deep Architectures for AI. Foundations and Trends in Machine Learning, V2 (1), 2009.
David J.C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
Yoshua Bengio, Ian J. Goodfellow, and Aaron Courville. Deep Learning. 2015.

Prerequisites

Programming and other basic CS skills

C, C++
Java, C#, .NET
Perl, Python

Theories

Data representation (e.g., vector space model, language model)
Basic probability theory(e.g., likelihood, conditional probability, posterior probability, Bayes)
Basic linear algebra (e.g., linear transformations, eigenvalues, least-squares best fit)