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)
Not Open to Freshmen.
The grade will be based on course projects.
|40409||S01||3:00 pm - 4:20 pm||T Th||Friend Center 203||Enrolled:5 Limit:16|