Machine Learning (CS60050)
Instructor: Sourangshu Bhattacharya
Class Schedule: WED(09:30-10:30) , THURS(08:30-09:30) , FRI(10:30-11:30) , FRI(11:30-12:30)
Classroom: CSE-108
Website: http://cse.iitkgp.ac.in/~sourangshu/cs60050.html
First Meeting: Wednesday, 24th July, at 09:30 am in CSE-108.
Syllabus:
Basic Principles: Introduction, The concept learning task.
General-to-specific ordering of hypotheses. Version spaces. Inductive bias.
Experimental Evaluation: Over-fitting, Cross-Validation.
Supervised Learning: Decision Tree Learning. Instance-Based Learning:
k-Nearest neighbor algorithm, Support Vector Machines, Ensemble learning: boosting, bagging.
Artificial Neural Networks: Linear threshold units, Perceptrons,
Multilayer networks and back-propagation.
Probabilistic Models: Maximum Likelihood Estimation, MAP, Bayes Classifiers Naive Bayes.
Bayes optimal classifiers. Minimum description length principle. Bayesian Networks,
Inference in Bayesian Networks, Bayes Net Structure Learning.
Unsupervised Learning: K-means and Hierarchical Clustering, Gaussian Mixture Models,
EM algorithm, Hidden Markov Models.
Computational Learning Theory: probably approximately correct (PAC) learning.
Sample complexity. Computational complexity of training. Vapnik - Chervonenkis dimension,
Reinforcement Learning.
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.