Course Details

Subject {L-T-P / C} : EE4601 : Introduction to Machine Learning {3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Dipti Patra


Introductory Topics, Linear Regression and Feature Selection, Linear Classification, Support Vector Machines, Artificial Neural Networks, Bayesian Learning, Decision Trees, Evaluation Measures, Hypothesis Testing, Ensemble Methods, Clustering, Graphical Models, Learning Theory and Expectation Maximization, Reinforcement Learning, Deep Learning

Course Objectives

  1. To be able to formulate machine learning problems corresponding to different applications.
  2. To understand a range of machine learning algorithms along with their strengths and weaknesses.
  3. To understand the basic theory underlying machine learning
  4. To be able to apply machine learning algorithms to solve problems of moderate complexity.

Course Outcomes

At the end of the course, students will be able to
1. Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc.
2. Have an understanding of the strengths and weaknesses of many popular machine learning approaches.
3. Appreciate the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning.
4. Be able to design and implement various machine learning algorithms in a range of real-world applications.

Essential Reading

  1. Tom M Mitchell, Machine Learning, PHI , 2015
  2. Ethem Alpaydin, Introduction to Machine Learning, The MIT Press , 3rd Edition 2015

Supplementary Reading

  1. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Introduction to Statistical Learning, Springer, 2013
  2. Richard Duda, Peter Hart, David Stork, Pattern Classification, John Willey

Journal and Conferences

  1. IEEE Transaction on Pattern Analysis and Machine Intelligence
  2. IEEE Transaction on Neural Networks & Learning Systems