Learning Objectives

To learn about major disciplines in artificial intelligence, their fundamental differences and applicability.

Learning Outcomes

Students will be able to state and apply major algorithms, methods, and theoretical results in the field of artificial intelligence.

Syllabus

Introduction: What is AI, agent, environment and its Applications.

Problem solving by search: principles of search, uninformed (“blind”) search, informed (“heuristic”) search, constraint satisfaction problems, adversarial search and games

Optimization methods: gradient descent, multi-objective optimization.

Knowledge representation and reasoning: rule based representations, declarative or logical formalisms, Logic Programing and logic network.

Reasoning in uncertain environments: Genetic algorithms, fuzzy logic, soft computing;

Learning: Supervised learning, unsupervised learning, reinforcement learning. Generative discriminative models.

Probabilistic models: Bayesian models, probabilistic discriminative models.

Discussion of practical cases from various domains: natural language processing, computer vision, bioinformatics etc.

Text books

  1. Artificial Intelligence : A Modern Approach (Paperpack). Stuart Russell and Peter Norvig. Pearson; 3 edition. 2010 ISBN-13: 978-0132071482
  2. Fundamentals of the New Artificial Intelligence. Toshinori Munakata. Springer Science & Business Media. ISBN 978-1-84628-839-5
  3. Pattern Recognition and Machine Learning. Christopher Bishop. Springer. 2006. ISBN-13 978-0-387-31073-2.\
  4. Artificial Intelligence (Third Edition).Elaine Rich,Kevin Knight,Shivashankar B. Nair. Tata McGraw-Hill Education Pvt. Ltd.. 2008. ISBN 13: 9780070087705
  5. Reinforcement Learning: An Introduction. Richard S. Sutton Andrew G. Barto . MIT Press, 2017. ISBN-13: 9780262332767

References

  1. Genetic Algorithms in Search, Optimization, and Machine Learning. David E. Goldberg. Pearson Education, 2006. ISBN-13: 9788177588293.
  2. Principles Of Artificial Intelligence. N.J. Nilsson. Narosa Book Distributors. 2002.ISBN-13: 978-8185198293
  3. Probabilistic Programming & Bayesian Methods for Hackers. Addison-Wesley Data and Analytics. ISBN-13: 978-0133902839.
  4. Introduction to Information Retrieval South Asian Edition. Christopher D. Manning, Hinrich Schütze, and Prabhakar Raghavan. Cambridge University Press. 2008. ISBN-13: 978-1107666399.
  5. Teaching statistics a bag of tricks. Andrew Gelman and Deborah Nolan. Oxford University Press, 2002. ISBN-13: 9780198572244.
  6. Advanced Methods for Knowledge Discovery from Complex Data. Sanghamitra Bandyopadhyay. Springer Science & Business Media, 2005. ISBN-13: 9781852339890.
  7. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Aurélien Géron. O'Reilly Media; 1 edition (April 9, 2017). ISBN-13: 978-1491962299.
  8. Deep Learning. Ian Goodfellow, Yoshua Bengio, Aaron Courville. The MIT Press, 2016. ISBN-13: 978-0262035613.
  9. Bayesian Data Analysis. Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. Third Edition. 2013. ISBN-13: 978-1439840955.

Metadata

Proposing Faculty: Department: Computer Science and Engineering Programme: B.Tech Proposing date: Approved date: Proposal type: Offerings: