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:
S. No. Name Affiliation
1 Bilol Banerjee (BB1) PhD student at ISI Kolkata
2 Mayukh Choudhury (MC) PhD student at IIT Bombay
3 Sourav Chakrabarty (SC) PhD student at ISI Kolkata
4 Debika Ghosh (DG) PhD student at IIM Udaipur
5 Annesha Deb (AD) PhD student at IIT Kanpur
6 Narayan Biswas (NB) PhD student at IISER Kolkata
7 Rajiv Misra (RM) PhD student at IISER Kolkata
8 Bishal Bhunia (BB2) PhD student at IISER Kolkata
9 Jitender Sharma (JS) PhD student at IISER Kolkata

 

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

 

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