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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.