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The course will cover machine learning techniques & applications. Machine learning techniques topics: a) adaptive techniques for feature selection & dimension reduction, b) unsupervised cluster discovery: K-means, SOFM, hierarchical clustering, c) supervised classifiers e.g. linear discriminant analysis (LDA), support vector machines, & d) kernel-based clustering/classification techniques. Genomic and multimedia applications will be explored.