Courses » Introduction to Machine Learning

Introduction to Machine Learning


With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.

This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD

We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well.

Any company in the data analytics/data science/big data domain would value this course.


VuNet Systems( www.vunetsystems.com ) brings in a big data approach to manage the complex IT infrastructure of enterprises. With its powerful analytics and intuitive visualisations, it helps connect the 1000s of dots in an IT infrastructure to keep it always on and secure. VuNet has customers across verticals, from banks, manufacturing, consumer care to IT/ITES, with some leading retail payment companies as well.  VuNet has also been recognised among the NASSCOM Emerge50 innovative product startups and is also part of the Cisco Launchpad program - Cisco’s partnership program with top emerging startups.

We are always on the lookout for talented programmers and will interview the course toppers( top 5% ), who are interested in an internship/job opportunity. Upon completion of this course, the toppers can submit their resumes and programming code samples. VuNet will interview the candidates and offer internships or job opportunities based on the interview.


Prof. Balaraman Ravindran is currently an associate professor in Computer Science at IIT Madras. He has nearly two decades of research experience in machine learning and specifically reinforcement learning. Currently his research interests are centered on learning from and through interactions and span the areas of data mining, social network analysis, and reinforcement learning.

Week 1: Introductory Topics
Week 2: Linear Regression and Feature Selection
Week 3: Linear Classification
Week 4: Support Vector Machines and Artificial Neural Networks
Week 5: Bayesian Learning and Decision Trees
Week 6: Evaluation Measures
Week 7: Hypothesis Testing
Week 8: Ensemble Methods
Week 9: Clustering
Week 10: Graphical Models
Week 11: Learning Theory and Expectation Maximization
Week 12: Introduction to Reinforcement Learning

  • The exam is optional for a fee. Exams will be held on 23 April 2017
  • Time: Shift 1: 9am-12 noon; Shift 2: 2pm-5pm
  • Any one shift can be chosen to write the exam for a course.
  • Registration URL: Announcements will be made when the registration form is open for registrations.
  • The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published.


  • Final score will be calculated as : 25% assignment score + 75% final exam score
  • 25% assignment score is calculated as 25% of average of best 8 out of 12 assignments
  • E-Certificate will be given to those who register and write the exam and score greater than or equal to 40% final score. Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras. It will be e-verifiable at nptel.ac.in/noc.


1. T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning, 2e, 2008.
2. Christopher Bishop. Pattern Recognition and Machine Learning. 2e.