Buddhananda Banerjee
(Ph.D in Statistics, ISI Kolkata)
Assistant Professor
N309A, Department of Mathematics
Indian Institute of Technology Kharagpur
Medinipur, West BengalIndia. PIN: 721302Email: bbanerjee@maths.iitkgp.ac.inEmbedded Files
Research Interest
Statistics on manifolds
Functional data analysis
Change point analysis
Surrogate analysis in Clinical Trails
Directional data analysis
Topological data analysis
Books:
Black, K.: Business statistics: for contemporary decision making.
Miller, M. & Miller, I. : John E. Freund's Mathematical Statistics: With Applications.
Rice, John A. : Mathematical statistics and data analysis.
Lay, D. C.: Linear algebra and its applications.
Useful Links
Probability
Random variable and vector
Moments
Transformation of random variables
Sampling distributions
Law of large numbers (LLN)
Inference
MA30235+MA39211 : Advanced Machine Learning + Lab (Autumn,2026) Click for details
Focus areas: Deep learning architectures (ANN, RNN, CNN, GNN), Training methodologies and optimization, Applications across vision, sequential data, and graphs Decision-making frameworks through reinforcement, defussion and transformer.
Prerequisites: Fundamentals of AI & ML, Design & Analysis of Algorithms
Books:
Books:
Bishop, C. M., & Bishop, H. (2023). Deep learning: Foundations and concepts. Springer Nature.
Murphy, K. P. (2023). Probabilistic machine learning: Advanced topics. MIT press.
Pedrycz, W., & Chen, S. M. (2020). Deep learning: Concepts and architectures. Cham: Springer.
Islam, T. (2026). Hands-on Deep Learning: Building Models from Scratch. Springer Nature.
Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge Univ. Press.
MA60056/MA60280 : Regression and Time series Models (Spring,2026) Download Lecture Notes
Regression: Linear Algebra (Revision) , Testing of Hypothesis (Revision) , Linear models, Miticollinearity, Regularization, Generalized Linear models. Model acccuracy checking
Time-Series: Linear processes, Stationarity, Autocovariance, ACF, PACF, Estimation, ARIMA, SARIMA
Prerequisites: Probability & Statistics
Books:
Books:
Introduction to Linear Regression Analysis, 5th Edition: Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining
Regression Analysis by Example, 5th Edition Samprit Chatterjee, Ali S. Hadi
Time Series Analysis and Its Applications: With R Examples : Robert H. Shumway , David S. Stoffer
Introduction to Time Series and Forecasting : Brockwell, Peter J., Davis, Richard A.
MA60274 : AI & ML (Spring ,2026) [Get Lecture references]
[ I will cover the ML part only ]
Focus Areas:
Focus Areas:
AI: Graph structures and search strategies, including uninformed and heuristic-based algorithms such as BFS, DFS, A*, and game-theoretic decision-making using minimax and Monte Carlo tree search. Logical reasoning through propositional logic complements algorithmic problem-solving.
ML: Supervised and unsupervised learning with emphasis on clustering methods, regression models, gradient-based optimization, and neural networks, backpropagation. Evolutionary algorithms, genetic algorithms,
Prerequisites: Probability & Statistics , Linear Algebra
Books:
Books:
Artificial Intelligence: A Modern Approach. Stuart J. Russell and Peter Norvig.
Artificial Intelligence: Structures and Strategies for Complex Problem Solving : G. F Lunger
Pattern recognition and machine learning. : C. M. Bishop,
An introduction to statistical learning with applications in R :G James, D Witten, T Hastie, and R Tibshirani
Handbook Statistical foundations of machine learning: Gianluca Bontempi
MA20205 : Probability and Statistics (Autumn,2025) Download Class Notes
Focus Areas : Probability : Definition & laws, Random variable, Modeling with Random Variables, Examples of discrete random variables, Examples of continuous random variables:, Joint and conditional distributions, Laws of expectation , Law of Large Numbers , Estimation , Testing of Hypothesis .
Focus Areas : Probability : Definition & laws, Random variable, Modeling with Random Variables, Examples of discrete random variables, Examples of continuous random variables:, Joint and conditional distributions, Laws of expectation , Law of Large Numbers , Estimation , Testing of Hypothesis .
Prerequisites: 1st year mathematics courses
Books:
Books:
Mathematical Statistics and Data Analysis by John A. Rice
Introduction to Probability and Statistics for Engineers and Scientists by S.M. Ross
Probability and Statistical Inference by R.V. Hogg, E.A. Tanis, & D. L. Zimmerman
Introduction to Probability Theory by Paul G. Hoel, Sidney C. Port and Charles J. Stone
MA60263 : Multivariate Statistical Methods (Autumn,2025) Download Lecture note
Focus Areas : Linear Algebra (Revision) , Joint & Marginal Probability distributions (Revision) , Multivariate Normal & sampling distributions , Multiple linear regression
Focus Areas : Linear Algebra (Revision) , Joint & Marginal Probability distributions (Revision) , Multivariate Normal & sampling distributions , Multiple linear regression
Principal Component analysis , Factor Analysis , Discriminant Analysis , Classification, Clustering , High dimensional data analysis
Prerequisites: Probability & Statistics , Linear Algebra
Books:
Books:
Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis (Vol. 5, No. 8).
Zelterman, D. (2015). Applied multivariate statistics with R
Boyd, S., & Vandenberghe, L. (2018). Introduction to applied linear algebra: vectors, matrices, and least squares.
Strang, G. (2020). Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares [Bookshelf].
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