Introduction to Machine Learning - IITKGP

Week by week (official course layout)

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

Topics as the course page lists them. Weekly assignment due dates are shown only inside the course after a SWAYAM sign-in, so they are not here — check the course page every week.

This course's certificate rule

Average assignment score = 25% of average of best 6 assignments out of the total 8 assignments given in the course. Exam score = 75% of the proctored certification exam score out of 100 Final score = Average assignment score + Exam score Please note that assignments encompass all types (including quizzes, programming tasks, and essay submissions) available in the specific week. YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

As printed on the course page. General rules: NPTEL FAQ.