Teaching assistants: Akash Kumar, Siddarth R, Nisarg Patel
Assignments 40%, midsemester exam 20%, final exam 40%
Copying is fatal
Textbook and reading material
Data Mining; Concepts and Techniques by Jiawei Han and Micheline Kamber.
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.
Here is a tentative list of topics.
Supervised learning: Frequent itemsets, association rules, decision trees, naive Bayes, SVM, classifier evaluation, expectation maximization, ensemble classifiers.
Unsupervised learning: Clustering, outlier detection.
Text mining: Basic ideas from information retrieval, TF/IDF model, Page Rank, HITS
Other topics: Probabilistic graphical models, Bayesian networks, Markov models, neural networks, ranking and social choice, …
Lecture 1, 08 Aug 2017:
Frequent itemsets, a-priori algorithm
Lecture 2, 10 Aug 2017:
A-priori algorithm, association rule generation, tabular data, multiple minimum supports, class association rules
Lecture 3, 17 Aug 2017:
Lecture 4, 22 Aug 2017:
Discretizing continuous attributes
Overfitting and tree pruning
Lecture 5, 24 Aug 2017:
Naive Bayesian Classifiers
Generative probablisitic models and parameter estimation, naive Bayes text classifiction
Lecture 6, 29 Aug 2017:
Support vector machines (SVMs), the linearly separable case
Lecture 7, 31 Aug 2017:
SVMs with soft margins, kernel functions
Lecture 8, 5 Sep 2017:
A formal setting for machine learning, online learning, Perceptron algorithm, VC-dimension
Lecture 9, 7 Sep 2017:
True error and sample error, sample size vs overfitting, VC-dimension, ensemble classifiers: bagging and boosting
Lecture 10, 12 Sep 2017:
Clustering: K-Means, hierarchical
Lecture 11, 19 Sep 2017:
Density based clustering
Density based local outlier detection
Semi supervised learning: Expectation-Maximization