Machine Learning (CS60050)
Instructor: Sourangshu Bhattacharya
Class Schedule: WED(11:30-12:30) , THURS(10:30-11:30) , FRI(08:30-09:30) , FRI(09:30-10:30)
Classroom: CSE-119
Website: http://cse.iitkgp.ac.in/~sourangshu/cs60050_14A.html
First Meeting: Thursday, 17th July, at 10:30 am in CSE-119.
Syllabus:
Basic Principles: Introduction, Experimental Evaluation: Over-fitting, Cross-Validation.
PAC learning. Sample complexity. VC-dimension, Reinforcement Learning.
Supervised Learning: Decision Tree Learning, k-NN classification, SVMs, Ensemble learning: boosting, bagging. Artificial Neural Networks: Perceptrons, Multilayer networks and back-propagation.
Probabilistic Models: Maximum Likelihood Estimation, MAP, Bayes Classifiers, Naive Bayes. Markov Networks, Bayesian Networks, Factor Graphs, Inference in Graphical Models.
Unsupervised Learning: K-means and Hierarchical Clustering, Gaussian Mixture Models, EM algorithm, Hidden Markov Models.
Textbooks:
- Tom Mitchell. Machine Learning. McGraw Hill, 1997.
- Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer 2006.
- Richard O. Duda, Peter E. Hart, David G. Stork. Pattern Classification. John Wiley & Sons, 2006.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer 2009.