Class overview
This is the website for the (online) course for M.Tech. in Computer Science at Indian Statistical Institute, Kolkata. Students who are taking this course should join the Google group to receive lectures links and updates.
Lecture schedule: Tuesdays, Thursdays and Saturdays from 12:20 PM -- 1:10 PM IST
Doubt clearing session: Fridays 2:15 PM -- 3:00 PM
Slides: [MapReduce and Hadoop] [Introduction to Spark]
Notebook: [Apache Spark Basics]
Slides: [Association Rule Mining]
Slides: [Locality Sensitive Hashing]
Slides: [Mining data streams]
Videos: [Lecture 8] [Lecture 9] [Lecture 10] [Lecture 12]
Notebooks: [Blooom filter] [Flajolet-Martin algorithm]
Resource: [words.txt]
Slides: [Link analysis]
Videos: [Lecture 11]
Slides: [Linear algebra background] [Random projection] [SVD and CUR Decomposition]
Videos: [Lecture 13] [Lecture 14] [Lecture 15] [Lecture 16] [Lecture 17]
Notebooks: [Linear transformations] [Random projection] [SVD] [Recommendation with SVD]
Slides: [Hierarchical clustering]
Videos: [Lecture 18]
Slides: [Mining social network graphs] [Spectral methods for clustering of graphs]
Videos: [Lecture 19] [Lecture 20] [Lecture 21] [Lecture 22]
Notebook: [Spectral Clustering]
Resource: [facebook_combined.txt]
Slides: [Advertising on the web]
Videos: [Lecture 23] [Lecture 24]
Slides: [Basics of search]
Videos: [Lecture 25] [Lecture 26]
Slides: [Multi-arm bandits] [MDP] [Dynamic programming] [Monte Carlo] [Temporal difference learning]
Videos: [Lecture 27] [Lecture 28] [Lecture 29] [Lecture 30] [Lecture 31] [Lecture 32] [Lecture 33] [Lecture 34]
Notebooks: [MC vs TD]
The quizzes are posted here as reference. For the students of this course, the quizzes have been conducted as per schedule and graded already.
Primary reference books
Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman and Jeff Ullman.
Reinforcement learning: An introduction by Richard S. Sutton and Andrew G. Barto.
Other resources and references are provided with the slides and notebooks of the respective topics.