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Course Offerings

Course Details

Spring 2006-2007
ELE 571  

Digital Neurocomputing

Sun-Yuan Kung

The course will focus on Machine Learning for Bioinformatics, for graduate students in engineering, biologic & genomic sciences. It covers machine learning techniques & explains how can they apply to bioinformatics. Topics are: (a) Overview of molecular biology, (b) Adaptive techniques for feature selection & dimension reduction include PCA, ICA, FDA, etc. (c) Adaptive cluster discovery: K-means, EM, SOFM, hierarchical clustering & genomic applications. (d) Adaptive classifiers such as BP, GMM, SVM, & genomic applications. (e) Multi-modal fusion to combine information from multiple biological & algorithmic modalities.

Sample reading list:
Pierre Baldi and Søren Brunak, Bioinformatics : the machine learning approach, 2nd Edition
SY Kung, M.W. Mak, S.H. Lin, Biometric Authentication: A Machine Learning Approach (2004)
Bernhard Schölkopf, Koji Tsuda, Jean-Philippe Vert, Kernel methods in computational biology (2004)

Other Requirements:
Not Open to Freshmen.

Other information:
The grade will be based on course projects.

Website:  http://http://www.blackboard.princeton.edu

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
40409 S01 3:00 pm - 4:20 pm T Th   Friend Center   203   Enrolled:5 Limit:16