Data Mining

Overview

This course on data mining will seek to blend two aspects of data mining. On the one hand, we will focus on some established data mining problems & techniques. Currently, I plan to teach link analysis (e.g., PageRank), clustering, and social network analysis. On the other hand, we will also seek to bring out the various computational models like the classical RAM model, streaming, Massively Parallel Computation, k-machine model, and other distributed computing models.

This course will err on the side of fewer topics that we study in depth than attempting any form of comprehensive coverage. There will be an emphasis on algorithm design and mathematical analysis.

Prerequisites.

You must have be comfortable with programming, algorithms, and data structures. Since this course will emphasize mathematical analysis, you must be comfortable with proof techniques (esp., in the context of algorithm design) and probability theory. If in doubt, email me. More details will be presented in the first day of class.

Course Details

Instructor: John Augustine (email: augustine at cse)

Lectures: Room CS34, L Slot (Thursday 2 - 3.15 PM and Friday 3.25 - 4.40 PM). First class meeting is on Thursday Jan 16.

Office Hours:

• Thursday 3.30 - 4.30 PM, Friday 4.45 - 5.45 (basically, an hour after each lecture).
• Virtual Tutorial Hours. I will frequently set aside time to answer questions about assignments. During that time, I will make my self available on the instant messaging service discussed below. When possible, I will also make myself physically available at my office.
• Otherwise, by appointment via email.

Textbooks: We will follow two textbooks. Latest versions are available freely in their respective web pages.

• [LRU] Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman.
• [BHK] Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravi Kannan.

Online Interactions: You have the following options to interact online in this course.

• Course web page. I will be updating material at regular intervals.
• Email me directly for things that pertain to you (marks, etc.).
• Instant Messaging: Post in the room mean't for this course hosted in the matrix server (address: #2020-CS6720:matrix.org ). The easiest way to do this is to create an account at Riot and send a message to me (address: @j.e.augustine:matrix.org) with your name and roll number. Then, I will add you to the course.
• Moodle. Assignments will be posted in Moodle. You will also have to submit via Moodle. So if you are not enrolled in the course at Moodle, please fill this form: https://forms.gle/oPGqAbVyiFDr3CY68

Tentative Topics

Problems and Techniques

• Link Analysis (PageRank, etc.)
• Social Network Analysis
• Clustering (k-means, etc.)

Computational Models

• Streaming
• Distributed Computing

Updated Grading Scheme

• Two quizzes (15+20 = 35marks)
• Quiz 1 (15 marks)
• Short quizzes (2.5 marks times (best 4 out of 6) = 10 marks)
• Assignments (3 Assignments (3+6+6 ) = 15 marks)
• Take-home Final exam or Presentation (30 marks)
• In class final test (online for graduating students) (30 marks)

Due to the unfolding coronavirus situation, you should apply the following pseudocode.

All students: fill out the google form "Expressing your grading choices." In this form, you can choose whether you wish to write the take-home exam or make a presentation. You can also express tentative dates for making presentations. Last date for expressing your choices is May 31. 

All students: your In-class (or online) final test will be based on your batch (graduating/non-graduating, year, program, etc.). I will inform the date to you.

If you wish to write the take-home exam

    Ensure that the last assignment is submitted by June 03

Else /* i.e., you wish to make a paper presentation */

    If you are a graduating student

        Ensure both assignment and presentation are completed by June 20.

        /* I will provide more details shortly */

    Else /* i.e., you are not a graduating student */

        Ensure last assignment is submitted within a week after presentation

    Endif

Endif