Machine Learning (CS60050)


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

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 5
Classifier Evaluation
Chapter 5 of TM
Evaluation

Ensemble Learning, Boosting
Chapter 9 of DHS
Adaboost

Unsupervised Learning, Clustering
Chapter 10 of DHS
Clustering
Exercise 6
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



NPTEL Lecture Videos on Some Topics

Lecture Videos by Professor C A Murthy

Homework Assignments 2019:

Assignment 1      Data Set 1     

Assignment 2      Training Set 2     Test Set 2

Assignment 3      Training Set 3      Test Set 3

Assignment 4      Training Set 4       Test Set 4


Homework Assignments 2018:

Assignment 1      Data Set 1     

Assignment 2      Training Set 2     Test Set 2

Assignment 3      Training Set 3      Test Set 3

Assignment 4      Training Set 4       Test Set 4

Assignment 5     

Assignment 6      Training Set 6      Test Set 6

Assignment 7      Data Set 7      

Assignment 8     



Exams: Midsem2019  Endsem2019  Midsem2018  Endsem2018  Midsem2017  Endsem2017  Midsem2016 Endsem2016