AIS - Application of Statistical Learning and Stochastic Processes in Physical Domain
Speakers and 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