Syllabus :

Introduction to Machine Learning

Code: EE 523 | L-T-P-C : 3-0-0

Introduction to learning: Supervised and Unsupervised, Generative and Discriminative models, Classification and Regression problems; Feature selection, dimensionality reduction using PCA; Bayesian classification, Discriminative classifiers: Perceptrons, Multi-layer perceptron, RBF Networks, Decision Trees, Support Vector Machines; Unsupervised learning: EM Algorithm; K-Means clustering, DBSCAN, Hierarchical Agglomerative Clustering, Density estimation in learning, Mean-shift clustering; Classification performance analysis; Ensemble methods: Ensemble strategies, boosting and bagging; Sequence Models: Hidden Markov Models, Probabilistic Suffix Trees; Applications and Case studies.

Texts / References:

1. E. Alpaydin, Introduction to Machine Learning, 3rd Edition, Prentice Hall (India) 2015.
2. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd Edn., Wiley India, 2007.
3. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer, 2006.
4. S. O. Haykin, Neural Networks and Learning Machines, 3rd Edition, Pearson Education (India), 2016