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Sathyabama Institute of Science and Technology M.E. - Embedded and IoT SECA7017 AI and Soft Computing Syllabus

SATHYABAMA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL OF ELECTRICAL AND ELECTRONICS ENGINEERING

SECA7017 AI AND SOFT COMPUTING
L T P Credits Total Marks
3 0 0 3 100

UNIT 1 INTRODUCTION TO AI 9 Hrs.
History and Definition of AI, Foundations Intelligent Agents - Agents and environments-Good behavior - the nature of
environments, Structure of agents-Problem Solving agents, Searching for solutions - Uninformed search strategies- Breadth- first, depth-first, depth limited search, Uninformed search strategies - Iterative deepening DFS, Bi-directional search
strategies, Avoiding repeated states, searching with partial information.

UNIT 2 SEARCHING TECHNIQUES 9 Hrs.
Informed search and exploration- Informed search strategies, greedy best-first, A* Algorithm, Memory - bounded heuristic
search, heuristic functions, Local search algorithms and optimization problems, searching in continuous space, CSP -
backtracking search for CSPs, Local search for CSP - structure of problems, Adversarial search.

UNIT 3 KNOWLEDGE REPRESENTATION 9 Hrs.
Introduction to Logic, Syntax and semantics of first order logic, Using first order logic, assertions and queries in first-order
logic, kinship domain, Wumpus world problem, Knowledge engineering in first order logic, Inference in first order logic-
Propositional vs. first-order inference, Unification and lifting, Storage and retrieval, Forward chaining, Backward chaining,
Resolution, Knowledge representation.

UNIT 4 LEARNING 9 Hrs.
Introduction, Learning from observations, Inductive learning, Learning decision trees, Ensemble learning, logical formulation
of learning, Knowledge in learning, explanation based learning, Learning using relevance information, inductive logic
programming, Statistics learning methods, learning with complete data, Learning with hidden variables – EM algorithm,
Instance based learning, Introduction to Neural networks, Neural networks, learning neural network structures,
Reinforcement learning, passive reinforcement learning, Active reinforcement learning Generalization in reinforcement
learning.

UNIT 5 APPLICATIONS 9 Hrs.
Communication - Communication as action, A formal grammar for a fragment of English, Syntactic analysis Augmented
grammars, Semantic interpretation, Semantic interpretation, Ambiguity and disambiguation, Discourse understanding-
Grammar induction, Probabilistic language processing - Probabilistic language models, Information Retrieval and
implementation, Information Extraction, Machine translation systems
Max. 45 Hrs.

COURSE OUTCOMES
On completion of the course students will be able to
CO1 - Understand the various searching techniques, constraint satisfaction problem and example problems- game playing
techniques.
CO2 - Apply these techniques in applications which involve perception, reasoning and learning.
CO3 - Explain the role of agents and how it is related to environment and the way of evaluating it and how agents can act
by establishing goals.
CO4 - Acquire the knowledge of real world knowledge representation.
CO5 - Analyze and design a real world problem for implementation and understand the dynamic behavior of a system.
CO6 - Use different machine learning techniques to design AI machine and enveloping applications for real world
problems.

TEXT / REFERENCE BOOKS
1. Stewart Russell and Peter Norvig, "Artificial Intelligence-A Modern Approach ", Pearson Education, 2nd Edition, 2012.
2. Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”, Harcourt Asia Pvt. Ltd., 2015.
3. Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw-Hill, 2nd Edition, 2013.
4. George F. Luger, “Artificial Intelligence-Structures and Strategies for Complex Problem Solving”, Pearson Education,
1st Edition, 2002.

END SEMESTER EXAMINATION QUESTION PAPER PATTERN
Max. Marks: 100 Exam Duration: 3 Hrs.
PART A: 5 Questions of 6 Marks each – No choice 30 Marks
PART B: 2 Questions from each unit of internal choice, each carrying 14 Marks 70 Marks