Convener(s)
| Name: | Dr. Rajendra K. Ray | Dr. Tanmay Kayal |
| Mailing Address: | Professor & Chairperson, School of Mathematical and Statistical Sciences, Indian Institute of Technology Mandi |
Assistant Professor, School of Mathematical and Statistical Sciences, Indian Institute of Technology Mandi, |
| Email: | rajendra at iitmandi.ac.in | tanmay at iitmandi.ac.in |
This AIS aims to provide participants with a comprehensive introduction to statistical learning and stochastic processes, with a particular focus on their applications in physical systems. The program begins with foundational topics in probability, random variables, and key statistical concepts, setting the stage for understanding advanced methods. Participants will explore statistical learning techniques, including model selection, regularization methods, and common algorithms like linear regression and decision trees, emphasizing their role in supervised and unsupervised learning.
In addition, the school delves into stochastic processes, with a special emphasis on Markov processes and stochastic differential equations, illustrating their relevance in modelling physical phenomena. Through practical sessions using Python and MATLAB, participants will gain hands-on experience applying statistical learning and stochastic processes to address challenges in different areas of fluid dynamics.
Dates:
Venue:
Venue Address:
Indian Institute of Technology (IIT) Mandi, Tehsil: Sadar, VPO. - Kamand, Mandi-175005, Himachal Pradesh.
Venue State:
Venue City:
PIN:
Syllabus:
| Name of the Speaker with affiliation | No. of Lectures | Detailed Syllabus |
| Rajendra K. Ray, IIT Mandi | 3L + 2T | Basics of Probability: Introduction to random variables; Conditional probability and Bayes’ theorem; Common distributions: Bernoulli, Binomial, Poisson, Normal; Moments and their interpretations like expectation, variance, moments, etc.; Different types of convergence: almost sure, in probability, in distribution; Central Limit Theorem and Law of Large Numbers. |
| Tanmay Kayal, IIT Mandi | 3L + 2T | |
| Debasis Kundu, IIT Kanpur | 6L +4T | Introduction to Stochastic Processes: Definition and classification; Introduction to Markov Processes; Classification of Markov Processes; Discrete-Time Markov Chains; Continuous-Time Markov Chains. |
| Tanmay Sen, ISI Kolkata | 6L + 4T | Statistical Learning: Population; Sample; Point estimation; Interval estimation; Hypothesis testing (Parametric and Non-parametric); Bias-variance tradeoff; Model selection; Regularization techniques (Lasso, Ridge); Overview of common algorithms (Linear regression, Decision trees). |
| Subhasis Chaudhuri, IIT Bombay | 6L + 4T | Introduction to Machine Learning (ML) and Deep Learning (DL): Machine Learning Overview; Types of ML (Supervised, Unsupervised, Reinforcement Learning); Common Algorithms (Linear Regression, Decision Trees, KNN, SVM, etc.); Model Evaluation (Cross-validation, Overfitting, Underfitting); Introduction to Deep Learning; Neural Networks and Perceptrons; Deep Learning Architectures (CNNs, RNNs, GANs); Backpropagation and Gradient Descent; Applications of ML and DL (Image Recognition, Natural Language Processing, Autonomous Systems, etc.). |
| Soumyendu Raha, IISc Bangalore | 6L + 4T | Markov Processes and Physical Systems: Introduction Stochastic differential equations (e.g., Ito Calculus, Fokker-Planck equations, etc.); Applications of Markov processes in physical systems. |
| Soumyendu Raha, IISc Bangalore | 3L+2T | Introduction to Physics-Informed Neural Networks (PINNs): Overview of PINNs:Recent advancement in PINN algorithms; Applications of PINN in solving physical problems. |
| Rajendra K. Ray, IIT Mandi | 3L+2T |
References:
1. S. Ross. A First Course in Probability, Pearson.
2. J. Medhi. Stochastic Processes, New Age.
3. G. Casella and R. L. Berger. Statistical Inference, Cengage.
4. B. Oksendal. Stochastic Differential Equations: An Introduction with Applications, Springer 2003.
5. M. Nielsen, Neural Networks and Deep Learning, Determination Press, 2015.
(Lecture notes from the speakers will be provided, if available)
Tutorial Assistants:
-
S.No.
Name
Affiliation
1
ArindamSarkar
IITMandi
2
BiswanathBarman
IITMandi
3
ShrutiNaryal
IITMandi
4
PawanKumar Patel
IITMandi
Time Table:
-
Day
Date
Lecture1
(9.30–11.00)
Tea
(11.05 – 11.25)
Lecture2
(11.30–1.00)
Lunch
(1.05-2.25)
Tutorial
(2.30–3.30)Tea
(3.35-3.55)
Tutorial
(4.00-5.00)(Name of the
speaker)
(Name of the
speaker)
(Name of the speaker
+tutors)(Name of the speaker+
tutors)Mon
01/06/2026
RR
RR
RR,TK,PP
RR,TK,PP
Tues
02/06/2026
TK
TK
RR,TK,PP
RR,TK,PP
Wed
03/06/2026
TS
TS
TS,TK,PP
TS,TK,PP
Thu
04/06/2026
TS
TS
TS,TK,BB
TS,TK,BB
Fri 05/06/2026 SC SC SC, TK, SN SC, RR, SN Sat 06/06/2026 SC SC SC, TK, SN SC, RR, SN SUNDAY : OFF Mon
08/06/2026 DK DK DK, MK, SN DK, MK, SN Tues 09/06/2026 DK DK TK, AS, BB TK, AS, BB Wed 10/06/2026 SR SR SR, RR, AS SR, RR, AS Thu 11/06/2026 SR SR SR, RR, AS SR, RR, AS Fri 12/06/2026 SR SR SR, RR, SN SR, RR, BB Sat 13/06/2026 SR SR RR, TK, SN RR, TK, BB
SC:Subhasis Chaudhuri
DK: Debasis Kundu
SR: Soumyendu Raha
TS: Tanmay Sen
RR:RajendraK.Ray
TK: Tanmay Kayal
AS: Arindam Sarkar
BB:Biswanath Barman
SN: Shruti Naryal
PP:Pawan Kumar Patel
Selected Applicants:
How to Reach:
TBA