Machine Learning Course

Instructor: Pabitra Mitra, CSE, <pabitra@cse.iitkgp.ac.in> Phone: 03222-282356

Teaching Assistants:

Sinchani Chakraborty <sinchanichakraborty@gmail.com >

Sayantani Ghosh <gsayantani2012@gmail.com>

Anushri Saha<anusahaa2z@gmail.com>

Diangarti Tariang <diazz.tariang.89@gmail.com&>

S. Nishok Kumar <esunnishok@gmail.com>

Tanay Bhartia <tanaybhartia@gmail.com>

Manish Kumar <manish143ganesh@gmail.com>

Course Outline:

Machine learning is concerned with the question of how to make computers learn from experience. The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. For examples, machine learning techniques are used to create spam filters, to analyze customer purchase data, or to detect fraud in credit card transactions. The field of Machine Learning, which addresses the challenge of producing machines that can learn, has become an extremely active, and exciting area, with an ever expanding inventory of practical (and profitable) results, many enabled by recent advances in the underlying theory. This course will introduce the fundamental set of techniques and algorithms that constitute machine learning.

Books:

Machine Learning - Tom Mitchell (TM)

Pattern Classification - Duda, Hart and Stork (DHS)

Indtroduction to Machine Learning - E. Alpaydin (EA)

The Elements of Statistical Learning - Hastie, Tibshirani, Friedman (HTF)

Lecture Schedule:

Topic	Text Book	Slides	Exercise
Introduction, Learning Paradigms	Chapter 1 Tom Mitchell book (TM), Chapter 1 of Duda, Hart, Stork book (DHS)	Introduction
Concept Learning	Chapter 2 of TM	Concept Learning	Exercise 1
Decision Tree	Chapter 3 of TM	Decision Trees	Exercise 2
Bayes Classifier	Chapter 6 of TM	Bayes Classifier	Exercise 3
Bayesian Networks	Chapter 6 of TM	Bayes Net	Exercise 4
k-Nearest Neighbor	Chapter 8 of TM	KNN
Support Vector Machines	Chapter 5.11 of DHS	SVM	Exercise 5
Kernel Machines	Chapter 5.11 of DHS	Kernel Machines
Neural Networks, Perceptron	Chapter 4 of TM	ANN
Multilayered Perceptron	Chapter 6 of DHS	ANN	Exercise 6
Classifier Evaluation	Chapter 5 of TM	Evaluation
Ensemble Learning, Boosting	Chapter 9 of DHS	Adaboost
Unsupervised Learning, Clustering	Chapter 10 of DHS	Clustering	Exercise 7
Dimensionality Reduction	Chapter 3.7 of DHS	Dimensionality Reduction
Reinforcement Learning	Chapter 13 of TM	RL
Introduction to Learning Theory	Chapter 7 of TM	COLT