Teaching assistants: Deepanshi, Devesh Bajaj, Srishti Lakhotia
Evaluation:
Assignments 30-40%, quizzes and midsemester exam 20-30%, final exam 40%
Copying is fatal
Course outline (tentative)
Supervised learning: Association rules, regression, decision trees, naive Bayes, SVM, classifier evaluation, expectation maximization, ensemble classifiers.
Unsupervised learning: Clustering, outlier detection, dimensionality reduction.
Text mining: Basic ideas from information retrieval, TF/IDF model, Page Rank, HITS
Other topics (if time permits): Probabilistic graphical models, Bayesian networks, Markov models, neural networks, ranking and social choice, …
Text and reference books:
Web Data Mining by Bing Liu, 2nd edition, Springer (2011).
Foundations of Data Science by Avrim Blum, John Hopcroft and Ravi Kannan
Machine Learning by Tom Mitchell.
C4.5: Programs for Machine Learning by Ross Quinlan.
Artificial Intelligence: A Modern Approach by Stuart J Russell and Peter Norvig, 3rd edition, Pearson (2016).
Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow by Aurélien Géron, 3rd edition, O'Reilly (2022)
Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto, MIT Press, 2nd ed (2018)
TBA
Lecture 1: 6 Jan 2026
(Class Notes (pdf))
Introduction to supervised and unsupervised learning