EECS282 Advanced Topics in Machine Learning (Fall semester 2010)


Miguel Á. Carreira-Perpiñán
Associate professor
Electrical Engineering and Computer Science
School of Engineering
University of California, Merced
mcarreira-perpinan-[at]; 209-2284545
Office: 284, Science & Engineering Building

Office hours: by appointment (call or email, including [EECS282] in the subject).

Lectures: Tuesdays/Thursdays 10:30-11:45am (Classroom Building 274)

Lab class: Mondays 10am-12:50pm (Linux Lab, SE138)

Course web page:

Course description

The course reviews advanced topics in machine learning. Machine learning is the study of models and algorithms that learn information from data. Machine learning ideas underlie many algorithms in computer vision, speech processing, bioinformatics, robotics, computer graphics and other areas. The 2010 edition of the course will focus on dimensionality reduction and manifold learning and extend the contents of the 2008 edition.

Prerequisites: the course is intended for graduate students who have taken an introductory course in machine learning (such as EECS276).



There is no required textbook. Selected readings will appear in this web page in due course. The following are two reviews of dimensionality reduction and manifold learning techniques:

Other books on general machine learning:


Dimensionality reduction and manifold learning links

Matlab tutorials

If you have never used Matlab, there are many online tutorials, for example:

Other links

Miguel A. Carreira-Perpinan
Last modified: Sat Oct 1 21:23:21 PDT 2011

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