CS 725 - Foundations of Machine Learning
Supervised learning: decision trees, nearest neighbor classifiers, generative classifiers like naive Bayes, linear discriminant analysis, loss regularization framework for classification, Support vector Machines Regression methods: least-square regression, kernel regression, regression trees Unsupervised learning: k-means, hierarchical, EM, non-negative matrix factorization, rate distortion theory.
Hastie, Tibshirani, Friedman The elements of Statistical Learning Springer Verlag
Pattern recognition and machine learning by Christopher Bishop.
Remedial co-requisite: Mathematical foundations (Separately proposed by Prof. Saketh Nath) Recommended parallel courses: CS709 (Convex optimization)
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