MTech Artificial Intelligence syllabus for 3 Sem 2020 scheme 20MAU332

Module-1 Introduction 0 hours

Introduction:

Overview of AI problems, examples of successful recent AI applications. The Turing test, Rational versus non-rational reasoning.

Search Strategies:

Problem spaces (states, goals and operators), problem solving by search. Uninformed search (breadth-first, depthfirst, depth first with iterative deepening). Heuristics and informed search (hill-climbing, generic best-first, A*). Minimax Search, Alphabeta pruning

Module-2 Knowledge representation and reasoning 0 hours

Knowledge representation and reasoning:

Review of propositional and predicate logic, First order logic, Resolution and theorem proving, Forward chaining, Backward chaining, Temporal and spatial reasoning. Review of probabilistic reasoning, Bayes theorem. Totally-ordered and partially-ordered Planning

A d v e r t i s e m e n t
Module-3 Planning-The blocks world 0 hours

Planning-The blocks world, Components of Planning Systems, Goal stack planning, Nonlinear planning, Hierarchical planning. LearningLearning from example, Learning by advice, Explanation based learning, Learning in problem solving, Definition and examples of broad variety of machine learning tasks, Classification, Inductive learning, Simple statistical-based learning such as Naive Bayesian Classifier, decision trees.

Module-4 Natural Language Processing 0 hours

Natural Language Processing:

Language models, n-grams, Vector space models, Bag of words, Text classification, Information retrieval, Pagerank, Information extraction, Question-answering

Module-5 Agents 0 hours

Agents:

Definition of agents, Agent architectures (e.g., reactive, layered, cognitive), Multi-agent systems- Collaborating agents, Competitive agents, Swarm systems and biologically inspired models. Expert Systems: Representing and Using Domain Knowledge, Expert System Shells, Explanation, Knowledge Acquisition.

 

Course outcomes:

At the end of the course the student will be able to:

CO1: Understand, identify and apply the artificial learning concepts in automotive Vehicles

CO2:Differentiate search strategies and able to choose the one based on application

CO3:Explain different reasoning and planning technique and able to use them appropriately

CO4:Describe natural language processing and do information retrieval

CO5: Explain the expert systemand able to represent and use domain knowledge

 

Question paper pattern:

The SEE question paper will be set for 100 marks and the marks scored will be proportionately reduced to 60.

  • The question paper will have ten full questions carrying equal marks.
  • Each full question is for 20 marks.
  • There will be two full questions (with a maximum of four sub questions) from each module.
  • Each full question will have sub question covering all the topics under a module.
  • The students will have to answer five full questions, selecting one full question from each module.

 

Textbook/ Textbooks

(1) Elaine Rich, Kevin Knight and Shivashankar BNair, Artificial Intelligence Tata McGraw Hill3rd Edition 2009

(2) Dan W. Patterson, Introduction to Artificial Intelligence and Expert Systems Pearson Education1st Edition, 2015

 

Reference Books

(1) S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach Prentice Hall, 3rd Edition 2009

(2) Melanie Mitchel, Artificial Intelligence: A Guide for Thinking Humans Farrar, Straus and Giroux ,1st Edition, 2019

(3) Masoud Yazdani, Artificial Intelligence: Principles and Applications, Chapman and Hall, 1986 Digital Edition,2008

 

Note: In case expertise is not available in the parent department, CSE or ECE department faculty shall handle this course

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