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:

1. Bishop, C. M., & Bishop, H. (2023). Deep learning: Foundations and concepts. Springer Nature.

2. Murphy, K. P. (2023). Probabilistic machine learning: Advanced topics. MIT press.

3. Pedrycz, W., & Chen, S. M. (2020). Deep learning: Concepts and architectures. Cham: Springer.

4. Islam, T. (2026). Hands-on Deep Learning: Building Models from Scratch. Springer Nature.

5. Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge Univ.  Press.

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:

1. Artificial Intelligence: A Modern Approach. Stuart J. Russell and Peter Norvig.

2. Artificial Intelligence: Structures and Strategies for Complex Problem Solving : G. F Lunger 

3. Pattern recognition and machine learning. :  C. M. Bishop, 

4. An introduction to statistical learning with applications in R :G James, D Witten, T Hastie, and R  Tibshirani

5. Handbook Statistical foundations of machine learning:  Gianluca Bontempi

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:

1. Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis (Vol. 5, No. 8).

2. Zelterman, D. (2015). Applied multivariate statistics with R 

3. Boyd, S., & Vandenberghe, L. (2018). Introduction to applied linear algebra: vectors, matrices, and least squares. 

4. Strang, G. (2020). Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares [Bookshelf].

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:

1. Introduction to Linear Regression Analysis, 5th Edition: Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining

2. Regression Analysis by Example, 5th Edition Samprit Chatterjee, Ali S. Hadi

3. Time Series Analysis and Its Applications: With R Examples : Robert H. Shumway , David S. Stoffer

4. Introduction to Time Series and Forecasting : Brockwell, Peter J., Davis, Richard A.

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:

1. Mathematical Statistics and Data Analysis by John A. Rice 

2. Introduction to Probability and Statistics for Engineers and Scientists by S.M. Ross

3. Probability and Statistical Inference by R.V. Hogg, E.A. Tanis, & D. L. Zimmerman

4. Introduction to Probability Theory by Paul G. Hoel, Sidney C. Port and Charles J. Stone