Mehta Family School of Data Science and Artificial Intelligence

Indian Institute of Technology Guwahati

DA243

Introduction to Optimization

3-0-0-6

Pre-Requisite: None

Preamble / Objectives (Optional):

Course Content/ 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:

1. E. K. P. Chong and S. H. Zak, An Introduction to Optimization, 4^th Edition, Wiley India Pvt. Ltd., 2017.

2. D. G. Luenberger and Y. Ye, Linear and Nonlinear Programming, 4^th Edition, Springer, 2016.

References: (Format: Authors, Book Title in Italics font, Volume/Series, Edition Number, Publisher, Year.)

1. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge India, 2016.

Pre-Requisite:none

Preamble / Objectives (Optional): This course covers the basics of artificial intelligence. Moreover, reinforcement learning concepts are also covered to familiarize students with current advances in artificial intelligence.

Course Content/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.

The lectures will focus on the well-established algorithms in these topics, and the laboratory exercises will supplement those lectures with programming assignments and mini projects.

Textbooks:

1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4^th Edition, Pearson, 2020.

2. R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction, 2^nd Edition, MIT Press, 2018.

3. I. Bratko, PROLOG Programming for Artificial Intelligence, 4^th Edition, Pearson, 2011.

References:

1. E. Rich and K. Knight, Artificial Intelligence, 3^rd Edition, McGraw Hill, 2017.

2. D. Khemani, A First Course in Artificial Intelligence, 6^th reprint Edition, McGraw-Hill Education, 2018.

3. Chris Meyers, Prolog in Python, https://www.openbookproject.net/py4fun/prolog/intro.html, 2009.

Pre-Requisite: none

Course Content/ 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:

1. Charles Dierbach, Introduction to Computer Science using Python: A Computational Problem-Solving Focus, 1^st Edition, Wiley, 2015.

2. Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers, How to Think Like a Computer Scientist: Learning with Python 3, 3^rd Edition, https://openbookproject.net/thinkcs/python/english3e, 2012.

Pre-Requisite: none

Preamble / Objectives (Optional):

Course Content/ 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:

1. R. Ramakrishnan, J. Geherke, Database Management Systems, 3^rd Edition, McGraw Hill, 2014.

References:

1. H. Garcia-Molina, J. Ullman, J. Widom, Database System: The Complete Book, 2^nd Edition, Pearson, 2013.

2. P. Bailis, J. Hellerstein, M. Stonebraker, Readings in Database Systems, 5^th Edition, http://www.redbook.io/pdf/redbook-5th-edition.pdf, 2015.

Pre-Requisite: none

Course Content/ Syllabus:

Programming laboratory will be set in consonance with the material covered in lectures. This will include database application development using SQL and front-end tools.

Textbooks:

1. R. Ramakrishnan, J. Geherke, Database Management Systems, 3^rd Edition, McGraw Hill, 2014.

References:

1. H. Garcia-Molina, J. Ullman, J. Widom, Database System: The Complete Book, 2^nd Edition, Pearson, 2013.

2. P. Bailis, J. Hellerstein, M. Stonebraker, Readings in Database Systems, 5^th Edition, available under Creative Commons Attribution-Non Commercial-Share Alike 4.0 license, http://www.redbook.io/pdf/redbook-5th-edition.pdf, 2015.

Pre-Requisite:none

Course Content/ 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:

1. J. A. Gubner, Probability and Random Processes for Electrical and Computer Engineers, 1^st Edition, Cambridge University Press, 2006.

2. S.M. Ross, A First Course in Probability, 10^th Edition., Pearson, 2019.

3. A. Papoulis, and S. U. Pillai, Probability, Random Variables and Stochastic Processes, 4^th Edition, Tata McGraw Hill, 2017.

References:

1. S. M. Ross, Stochastic Processes, 2^nd Edition, John Wiley and Sons, 2008.

2. V. K. Rohatgi and A. K. Md. E. Saleh, An Introduction to Probability and Statistics, 3^rd Edition, Wiley, 2015.