Course Code: DA321M	Course Name: Elements of Machine Learning	Credits: 3-0-0-6
Pre-requisite: None
Syllabus: Introduction to learning: supervised and unsupervised, generative and discriminative models, classification and regression problems, performance measures, design of experiments; Feature space and dimensionality reduction: distance measures, PCA, LDA; Unsupervised learning: K-means clustering, hierarchical agglomerative clustering, EM algorithm, Mixture model; Supervised learning: Bayesian classification, linear and logistic regression, simple perceptron and multi-layer perceptron, Parzen windows, k-nearest neighbor, decision trees, support vector machines; Hidden Markov models; Applications and case studies.
Textbooks: E. Alpaydin, Introduction to Machine Learning, 4th Ed., Prentice Hall (India) 2020. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd Ed., Wiley India, 2007.
References: C. M. Bishop, Pattern Recognition and Machine Learning, Information Science and Statistics, Springer, 2016. S. O. Haykin, Neural Networks and Learning Machines, 3rd Ed., Pearson Education (India), 2016.