Artificial Intelligence

GANPAT UNIVERSITY

FACULTY OF ENGINEERING & TECHNOLOGY

Programme

Bachelor of Technology

Branch/Spec.

Computer Science & Engineering (BDA/CBA/CS)

Semester

VI

Version

1.0.0.2

Effective from Academic Year

2022-23

Effective for the batch Admitted in

June 2020

Subject  code

2CSE60E14

Subject Name

Artificial Intelligence

Teaching scheme 

Examination scheme (Marks)

(Per week)

Lecture(DT)

Practical(Lab.)

Total

CE

SEE

Total

L

TU

P

TW

Credit 

3

0

1

0

4

Theory 

40

60

100

Hours

3

0

2

0

5

Practical

30

20

50

Pre-requisites:

Data structures, Algorithm design and analysis, Basic programming logic 

Learning Outcome:

Upon Completion of the course, the students will be able to 

• Learn the key components of the artificial intelligence (AI) field.

• Understand the key aspects of search strategies, planning and reasoning algorithms.

• Understand and apply the key aspects of statistical approach to solve computational problems. 

• Apply artificial intelligence techniques and algorithms to various use cases.

Theory syllabus

Unit

Content

Hrs

1

Basics of Artificial Intelligence

What is intelligence? Foundations of artificial intelligence (AI). History of AI; Problem Solving- Formulating problems, problem types, states and operators, state space, search strategies

5

2

Informed Search Strategies

Best first search, A* algorithm, heuristic functions, Iterative deepening A*(IDA), small memory A*(SMA)

5

3

Reasoning

Representation, Inference, Propositional Logic, predicate logic (first order logic), logical reasoning, forward chaining, backward chaining; AI languages and tools - Lisp, Prolog, CLIPS

7

4

Planning

Basic representation of plans, partial order planning, planning in the blocks world, hierarchical planning, conditional planning, representation of resource constraints, measures, temporal constraints

6

5

Statistical Reasoning 

Probability And Bayes’ Theorem, Certainty Factors And Rule-Base Systems, Bayesian Networks, DempsterShafer Theory, Fuzzy Logic.

7

6

Game Playing: 

Overview, And Example Domain : Overview, MiniMax, Alpha-Beta Cut-off, Refinements, Iterative deepening

3

7

Hidden Markov Models-

Markov Chains, The Hidden Markov Model, Likelihood Computation: The Forward Algorithm, Decoding: The Viterbi Algorithm, HMM Training: The Forward-Backward Algorithm

4

8

Learning

Inductive learning, decision trees, logical approaches, computational learning theory, reinforcement learning, natural language understanding and its Applications.

8

Practical content

Practicals will be based on developing intelligent agents through uninformed search strategy, informed search strategy, first order logic using prolog, Byesian network, automation for 2-Player game, Hidden Markov Chain, reinforcement learning, Natural language understanding.

Text Books

1

Stuart Russell and Peter Norvig. Artificial Intelligence – A Modern Approach, Pearson Education Press.

2

Kevin Knight, Elaine Rich, B. Nair, Artificial Intelligence, McGraw Hill.

3

Daniel Jurafsky, James H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

Reference Books

1

George F. Luger, Artificial Intelligence, Pearson Education.

2

Nils J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kauffman.

Course Outcomes:

Cos

Description

CO1

Learn the key components of the artificial intelligence (AI) field.

CO2

Understand the key aspects of search strategies, planning and reasoning algorithms.

CO3

Understand and apply the key aspects of statistical approach to solve computational problems.

CO4

Apply artificial intelligence techniques and algorithms to various use cases.

Mapping of CO and PO