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>
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.
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 |
NPTEL Lecture Videos on Some Topics
Lecture Videos by Professor C A Murthy
Homework Assignments 2019:Assignment 2 Training Set 2 Test Set 2
Assignment 3 Training Set 3 Test Set 3
Assignment 4
Data Set 4
Homework
Assignments 2018:
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 6 Training Set 6 Test Set 6
Exams: Midsem2019 Endsem2019 Midsem2018 Endsem2018 Midsem2017 Endsem2017 Midsem2016 Endsem2016