Created byDeepak Khemani
This is a first course on Artificial Intelligence. While the intended audience is both UG and PG students studying Computer Science, in fact anyone comfortable with talking about algorithms should be able to do the course.
INDUSTRY SUPPORT – LIST OF COMPANIES/INDUSTRY THAT WILL RECOGNIZE/VALUE THIS ONLINE COURSE
Any industry that is involved in development of AI applications. This not only includes software companies (like Microsoft, Google, and Facebook) but also manufacturing companies like Ford and General Electric, and retail companies like Amazon and Flipkart.
Exposure to data structures and programming and an ability to discuss algorithms is the only pre-requisite.
Overview and Historical Perspective, Turing Test, Physical Symbol Systems and the scope of Symbolic AI, Agents.
State Space Search, Heuristic Search, Solution Space Search, Stochastic Local Search, Population Based Methods.
Optimal Solutions, Algorithm A*, Admissibility of A*, Space saving variations of A*.
Problem Decomposition, Algorithm AO*, Rule Based Expert Systems, Rete Algorithm.
Game Playing: Algorithms Minimax, AlphaBeta, SSS*
Planning: Forward/Backward Search, Goal Stack Planning, Sussman’s Anomaly, Plan Space Planning, Algorithm Graphplan.
Text Book (Chapters 1-8): Deepak Khemani, A First Course in Artificial Intelligence, McGraw Hill (India), 2013
To access the content, please enroll in the course.
Course Syllabus & Schedule
Deepak Khemani is Professor at Department of Computer Science and Engineering, IIT Madras. He completed his B.Tech. (1980) in Mechanical Engineering, and M.Tech. (1983) and PhD. (1989) in Computer Science from IIT Bombay, and has been with IIT Madras since then. In between he spent a year at Tata Research Development and Design Centre, Pune and another at the then youngest IIT at Mandi. He has had shorter stays at several Computing departments in Europe.
Prof Khemani’s long-term goals are to build articulate problem solving systems using AI that can interact with human beings. His research interests include Memory Based Reasoning, Knowledge Representation and Reasoning, Planning and Constraint Satisfaction, Qualitative Reasoning, and Natural Language Processing.