CS 725 - Foundations of Machine Learning

Course content
  • 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.
  • Selected papers.
Pre-requisite : Remedial co-requisite: Mathematical foundations (Separately proposed by Prof. Saketh Nath) Recommended parallel courses: CS709 (Convex optimization)
Total credits : 6
Type : Theory
Duration :
Name(s) of other Academic units to whom the course may be relevant : N/A