Course Code: DA243	Course Name: Introduction to Optimization	Credits: 3-0-0-6
Pre-requisite: None
Syllabus: Introduction: Optimization problems and existence of optimal solutions, convex sets and convex functions; Unconstrained optimization: Basic properties of solutions and algorithms, gradient method, Newton’s method, conjugate direction method, quasi-Newton method; Linear optimization: Simplex algorithm, duality; Constrained optimization: Equality and inequality constraints, projected gradient method, penalty method; Convex optimization and duality, applications and algorithms.
Textbooks: E. K. P. Chong and S. H. Zak, An Introduction to Optimization, 4th Edition, Wiley India Pvt. Ltd., 2017. D. G. Luenberger and Y. Ye, Linear and Nonlinear Programming, 4th Edition, Springer, 2016.
References: S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge India, 2016.

Course Code: DA221	Course Name: Introduction to Artificial Intelligence	Credits: 2-0-2-6
Pre-requisite: None
Syllabus: Introduction to AI and Intelligent Agents; Problem solving by Searching: Uninformed and informed strategies and implementation; Path planning; Logical Agents: Propositional and first order logic, inference; Knowledge representation and Automated Planning; Prolog programming; Uncertain Knowledge and Reasoning: Quantifying uncertainty, probabilistic reasoning; Introduction to Reinforcement Learning (RL); Multi-armed Bandit, Ad Placement Problem; TD learning, Windy Gridworld Problem; Q-learning, Cliff Walking Problem; Policy Gradient; Applications & Case studies.
Textbooks: S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, Pearson, 2020. R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 2018. I. Bratko, PROLOG Programming for Artificial Intelligence, 4th Edition, Pearson, 2011.
References: E. Rich and K. Knight, Artificial Intelligence, 3rd Edition, McGraw Hill, 2017. D. Khemani, A First Course in Artificial Intelligence, 6th reprint Edition, McGraw-Hill Education, 2018. Chris Meyers, Prolog in Python, 2009.

Course Code: DA213	Course Name: Python Programming Laboratory	Credits: 0-0-3-3
Pre-requisite: None
Syllabus: Fundamental concepts: Variables and identifiers, data types, literals, operators, expressions; Conditional statements; Loops; Data structures: Lists, dictionaries and sets; Functions: Procedural and Recursive; Classes; Exception handling; File handling.
Textbooks: Charles Dierbach, Introduction to Computer Science using Python: A Computational Problem-Solving Focus, 1st Edition, Wiley, 2015. Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers, How to Think Like a Computer Scientist: Learning with Python 3, 3rd Edition, 2012.

Course Code: DA214	Course Name: Database Management Systems	Credits: 3-0-0-6
Pre-requisite: None
Syllabus: Relational DBMS: ER Model, relational model and algebras; Storage and file structure: Overview of secondary storage, RAID and flash storage, indexing (tree, hash, and bitmap), implementation of relational operators; SQL queries, constraints, triggers; Schema refinement and normal forms; Transaction management: ACID properties, concurrency control, crash recovery; Data warehousing and decision support.
Textbooks: R. Ramakrishnan, J. Geherke, Database Management Systems, 3rd Edition, McGraw Hill, 2014.
References: H. Garcia-Molina, J. Ullman, J. Widom, Database System: The Complete Book, 2nd Edition, Pearson, 2013. P. Bailis, J. Hellerstein, M. Stonebraker, Readings in Database Systems, 5th Edition, 2015.

Course Code: DA215	Course Name: Database Management Systems Laboratory	Credits: 0-0-3-3
Pre-requisite: None
Syllabus: Programming laboratory will be set in consonance with the material covered in DA214. This will include database application development using SQL and front-end tools.
Textbooks: R. Ramakrishnan, J. Geherke, Database Management Systems, 3rd Edition, McGraw Hill, 2014.
References: H. Garcia-Molina, J. Ullman, J. Widom, Database System: The Complete Book, 2nd Edition, Pearson, 2013. P. Bailis, J. Hellerstein, M. Stonebraker, Readings in Database Systems, 5th Edition, 2015.

Course Code: DA244	Course Name: Applied Probability and Random Processes	Credits: 3-0-0-6
Pre-requisite: None
Syllabus: Review of basic probability: Random variables and random vectors, Classical Inequalities and limit theorems; Random Number Generation; Generation of Random Variables: Inverse Transform method, Acceptance-rejection method, Variance Reduction methods: Control Variate, Conditioning, Importance Sampling; Uncertainty, Entropy. Random Processes: Definition and classification of random processes, Autocorrelation and properties, Random process through LTI systems, Bernoulli processes, Markov Chains (MCs): Preliminaries, Discrete-time MC: Transition Probability Matrix, Classification of states, Chapman-Kolmogorov Equation, Limiting & stationary Distributions, Ergodic MC; Continuous time MC: Poisson Process, Weiner process, Birth and Death Processes; Application and Case Studies.
Textbooks: J. A. Gubner, Probability and Random Processes for Electrical and Computer Engineers, 1st Edition, Cambridge University Press, 2006. S.M. Ross, A First Course in Probability, 10th Edition., Pearson, 2019. A. Papoulis, and S. U. Pillai, Probability, Random Variables and Stochastic Processes, 4th Edition, Tata McGraw Hill, 2017.
References: S. M. Ross, Stochastic Processes, 2nd Edition, John Wiley and Sons, 2008. V. K. Rohatgi and A. K. Md. E. Saleh, An Introduction to Probability and Statistics, 3rd Edition, Wiley, 2015.