Madhavan Mukund

Data Mining and Machine Learning,
Jan-May 2022

Data Mining and Machine Learning

Jan-May, 2022

Administrative details

  • Teaching assistants: Avirup Das, Ameya Kamat, Anuja Pal, Tanuj Sur

  • Lectures and Tutorials: Two live online classes each week. Zoom link on Moodle page. Tutorials as needed.

  • Evaluation:

    • Assignments 30-40%, quizzes and midsemester exam 20-30%, final exam 40%

    • Copying is fatal

  • Text and reference books:

    • Web Data Mining by Bing Liu.

    • 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.

    • Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow by Aurélien Géron, O'Reilly, 2nd edition (2019)

Course plan

Here is a tentative list of topics.

  • 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, …

Lecture summary