AIS - Statistical Learning Theory (2025)
Speakers and Syllabus
Name of the Speaker with affiliation | No. of Lectures | Detailed Syllabus |
Dr. Minerva Mukhopadhyay (ISI Kolkata) |
1L + 1T | Basics of Linear Algebra |
Dr. Satyaki Mazumder (IISER Kolkata) |
1L + 1T | Introduction to Bayesian Methods |
Dr. Debraj Das (IIT Bombay) |
3L + 3T | Probability Distributions, Some Useful Inequalities, Concentration Inequalities, Metrics and Divergences Between Probability Distributions |
Dr. Subhajit Dutta (ISI Kolkata) |
1L + 1T | Introduction to Statistical Learning |
Dr. Shyamal K. De (ISI Kolkata) |
4L + 4T | Primer on Functional Analysis and Probability on Hilbert Spaces, Clustering of Functional Data |
Dr. Moumita Das (IIM Udaipur) |
4L + 4T | Bayesian Machine Learning |
Dr. Gunjan Kumar (IIT Kanpur) |
3L + 3T | Distribution Testing: Uniformity Testing, Identity Testing, and Equivalence Testing |
Dr. Sutanu Gayen (IIT Kanpur) |
3L + 3T | Online Learning, Optimization |
Dr. Anirvan Chakraborty (IISER Kolkata) |
4L + 4T | Introduction to Functional Data, Classification of Functional Data |
Each lecture (L) will be of duration 2 hours. All 6 speakers (apart from the 3 organizers) are each delivering atleast 6 hours of lectures. Each tutorial (T) will be of duration 1 hour.
References:
- The Elements of Statistical Learning by Jerome Friedman, Robert Tibshirani and Trevor Hastie (2008).
- Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar, MIT, (2018).
- Probability: Theory and Examples by Rick Durrett (2019).
- Pattern Recognition and Machine Learning (PRML) by Christopher Bishop Springer, 2007.
- Machine Learning: A Probabilistic Perspective (MLAPP) by Kevin Murphy, MIT Press, 2012.
- Gaussian Processes for Machine Learning by Carl Rasmussen and Christopher Williams (2006).
- Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators by Tailen Hsing and Randall Eubank. Wiley Series in Probability and Statistics (2015).
- Understanding Machine Learning: From Theory to Algorithms by Shalev-Shwartz, Shai, and Shai Ben-David. Cambridge University Press, 2014.
- Prediction, Learning and Games by Cesa-Bianchi, Nicolo, and Gábor Lugosi. Cambridge University Press, 2006.
- A Survey on Distribution Testing: Your Data is Big. But is it Blue? by Canonne, Clément L. Theory of Computing (2020): 1-100.
- Testing that Distributions are Close by Batu, Tugkan, et al. Proceedings 41st Annual Symposium on Foundations of Computer Science. IEEE, 2000.
Time Table
Day | Date | Lecture 1 (9:30–11:30) |
Tea (11:30 to 12:00) |
Tutorial 1 (12:00-1.00) |
Lunch (1:00–2:30) |
Lecture 2 (2:30–4:30) |
Tea (4.30-5:00) | Tutorial 2 (5:00-6:00) |
Snacks (6:00-6:15) |
(name of the speaker) | (name of the speaker + tutor) | (name of the speaker) | (name of the speaker + tutor) | ||||||
Mon | 12.05.25 | MM | MM+AD | DD | DD+MC | ||||
Tues | 13.05.25 | DD | DD+MC | DD | DD+MC | ||||
Wed | 14.05.25 | SD | SD+AD | SM | SM+NB | ||||
Thu | 15.05.25 | SKD | SKD+SC | SKD | SKD+SC | ||||
Fri | 16.05.25 | AC | AC+BB1 | AC | AC+BB1 | ||||
Sat | 17.05.25 | AC | AC+BB2 | AC | AC+BB2 | ||||
SUNDAY: OFF | |||||||||
Mon | 19.05.25 | SKD | SKD+SC | SKD | SKD+SC | ||||
Tues | 20.05.25 | MD | MD+DG | MD | MD+DG | ||||
Wed | 21.05.25 | MD | MD+DG | MD | MD+DG | ||||
Thu | 22.05.25 | SG | SG+NB | SG | SG+NB | ||||
Fri | 23.05.25 | SG | SG+JS | GK | GK+JS | ||||
Sat | 24.05.25 | GK | GK+RM | GK | GK+RM |
Tutorial Assistants:
|
Full forms for the abbreviations of speakers:
DD: Debraj Das
MM: Minerva Mukhopadhyay
GK: Gunjan Kumar
SM: Satyaki Mazumder
SD: Subhajit Dutta
SG: Sutanu Gayen
MD: Moumita Das
SKD: Shyamal Krishna De
AC: Anirvan Chakraborty