MLDS

DS 216: Machine Learning for Data Science (MLDS)

Instructor: Prof. Vaanathi Sundaresan - vaanathi@iisc.ac.in

Class Timing: Tue/Thu 2:00 pm - 3:30 pm at CDS 102
Teams Joining Code: n3wlkiy

Description

This four credits course aims to cover machine learning techniques and statistical methods required for planning, developing and evaluating methods, especially applicable for various data analysis tasks. The course would also require students to implement programming assignments/projects related to these topics.

Prerequisites: Basic knowledge in linear algebra, probability and a good proficiency in programming (python) or a consent from the instructor.

Course TAs

Ramanujam Narayanan, M.Tech. Research, CDS - ramanujamn@iisc.ac.in
Vivek Dhamale, M.Tech., CDS - viveksd@iisc.ac.in
Anudeep, M.Tech. Research, CDS - archakams@iisc.ac.in
Biplab Nath, Ph.D., IAP- biplabnath@iisc.ac.in

Examination and Grading Policy

There will be three quizzes and two assignments with 10% weightage each.

A Mid-Term and End-Term examination with 25% each.

The schedule for the evaluations is as follows:

Assessment Date Weightage (%)

Quiz - I Feb 1st week, 2025 (06/02/2025) 10

Quiz - II March 1st Week, 2025 10

Quiz - III March 3rd week, 2025 10

Project-/problem-based assignment I Feb 2nd week, 2025 (Due: Feb 4th week, 2025) 10

Mid-Term exam Feb 18th, 2025 25

Project-/problem-based assignment II March 2nd week, 2025 (Due: March 4th week, 2025) 10

End-term exam April 21-30th, 2025 (Exact date: TBD) 25

Plagiarism, cheating on exams, and any other acts of academic dishonesty are serious offenses that not only compromise your educational development but also undermine the trust and respect essential to the scholarly community. Such actions are contrary to the values of our institution and will result in stringent consequences, which may include failing grades or further disciplinary measures.

Assignments & Announcements

To be updated

📢 Quiz 1 is scheduled for 6th February during regular class timings. Please be prepared and ensure timely attendance.

References

- C.M.Bishop, Pattern Recognition and Machine Learning, Springer, 2006,
- I. Goodfellow, Y. Bengio and A. Courville: Deep Learning, 2016.
- Jerome H. Friedman, Robert Tibshirani and Trevor Hastie, The Elements of Statistical Learning, Springer, 2001.
- R.O.Kuehl, Design of experiments: statistical principles of research design and analysis, 2000.
- Research papers, material/notes provided by the instructor.