Courses » Introduction to Machine Learning

Introduction to Machine Learning

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This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms. Feature reduction methods will also be discussed. We will introduce the basics of computational learning theory. In the course we will discuss various issues related to the application of machine learning algorithms. We will discuss hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies and cross-validation. The course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions.


  • Elective course
  • UG 
  • PG 
  • BE
  • ME
  • MS
  • MSc
  • PhD


  • Basic programming skills (in Python), algorithm design, basics of probability & statistics


Data science companies and many other industries value machine learning skills.

14833 students have enrolled already!!

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Course Instructor

Sudeshna Sarkar is a Professor and currently the Head in the Department of Computer Science and Engineering at IIT Kharagpur. She completed her B.Tech. in 1989 from IIT Kharagpur, MS from University of California, Berkeley, and PhD from IIT Kharagpur in 1995. She served briefly in the faculty of IIT Guwahati and at IIT Kanpur before joining IIT Kharagpur in 1998. Her research interests are in Machine Learning, Natural Language Processing, Data and Text Mining.

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Course layout
Week 1: Introduction: Basic definitions, types of learning, hypothesis space and inductive bias, evaluation, cross-validation
Week 2: Linear regression, Decision trees, overfitting
Week 3: Instance based learning, Feature reduction, Collaborative filtering based recommendation
Week 4: Probability and Bayes learning
Week 5: Logistic Regression, Support Vector Machine, Kernel function and Kernel SVM
Week 6: Neural network: Perceptron, multilayer network, backpropagation, introduction to deep neural network
Week 7: Computational learning theory, PAC learning model, Sample complexity, VC Dimension, Ensemble learning
Week 8: Clustering: k-means, adaptive hierarchical clustering, Gaussian mixture model
suggested reading

1. Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997.
2. Introduction to Machine Learning Edition 2, by Ethem Alpaydin


  • The exam is optional for a fee. Exams will be on 24 September 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 6 out of 8 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 KHARAGPUR. It will be e-verifiable at nptel.ac.in/noc