[vc_row el_class=”inner-body-content” css=”.vc_custom_1667210927638{padding-top: 30px !important;padding-bottom: 20px !important;}”][vc_column][vc_custom_heading text=”Pre-requisite(s)” font_container=”tag:h2|font_size:20px|text_align:left” use_theme_fonts=”yes” css=”.vc_custom_1667210912835{margin-top: 0px !important;}”][vc_column_text]Computer Programming (CS-1123)
Data Structures (CS-2143)[/vc_column_text][vc_custom_heading text=”Recommended Book(s)” font_container=”tag:h2|font_size:20px|text_align:left” use_theme_fonts=”yes”][vc_column_text]George F. Luger: Artificial Intelligence – Structures And Strategies For Complex Problem Solving, 6th Edition, Addison-Wesley Publishing Company, 2008, ISBN 978-0321545893[/vc_column_text][vc_custom_heading text=”Reference Book(s)” font_container=”tag:h2|font_size:20px|text_align:left” use_theme_fonts=”yes”][vc_column_text]Stuart J. Russell And Peter Norvig: Artificial Intelligence – A Modern Approach, 3rd Edition, Prentice-Hall Publishing Co., 2009, ISBN 978-0136042594.[/vc_column_text][vc_custom_heading text=”COURSE OBJECTIVES” use_theme_fonts=”yes”][vc_column_text]
By the end of this course, the students would be able to solve real-world problems using AI techniques. The students will also have good understanding of the various application areas of AI. They will become familiar with the current research in AI and the challenges currently being faced.
[/vc_column_text][vc_custom_heading text=”COURSE LEARNING OUTCOMES (CLO)” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes”][vc_column_text]Course Objectives[/vc_column_text][vc_custom_heading text=”COURSE CONTENTS” use_theme_fonts=”yes”][vc_custom_heading text=”Introduction” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes”][vc_column_text]Introduction to the course
Defining AI
Turing’s Test
Chinese Room Experiment
Application Areas of AI[/vc_column_text][vc_custom_heading text=” Agents in AI” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes”][vc_column_text]Agent Based Approach to AI
Types of Agents and their Environments
Introduction to Predicate Calculus
Inference Rules[/vc_column_text][vc_custom_heading text=” Concepts of Prolog” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes”][vc_column_text]Introduction to Prolog
Facts and Rules
Search and Unification
Backtracking
Recursion Based Search in Prolog
Using fail and cut Predicates to Control the Search
Using Lists
Implementing ADTs in Prolog[/vc_column_text][vc_custom_heading text=”Searching Algorithms” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes”][vc_column_text]Problem Solving by State Space Search
Formulating a Real World Problem as a State Space Search Problem
Depth-First and Breadth-First Search
Variations of Basic Search Algorithms
Informed Search
Heuristic Functions
Best-First Search Algorithms
A* Search[/vc_column_text][vc_custom_heading text=”Heuristic Functions” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes”][vc_column_text]Admissible and Monotonic Heuristics
Informedness of a Heuristic Function[/vc_column_text][vc_custom_heading text=” Game Programming” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes”][vc_column_text]Game Programming
Minimax Procedure
Alpha-Beta Pruning
Constraint Satisfaction Problems[/vc_column_text][vc_custom_heading text=” Expert Systems” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes”][vc_column_text]Introduction to Expert Systems
Design of rule-based Expert Systems
Knowledge Engineering and Knowledge Representation
Expert System Shells
Techniques for Managing Uncertainty in Expert Systems[/vc_column_text][vc_custom_heading text=” Language Analysis” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes”][vc_column_text]Natural Language Processing
Stages of Language Analysis
Chomsky’s Hierarchy of Grammars
Transition Network Parsers and Other Parsing Methods[/vc_column_text][vc_custom_heading text=” Concepts of Learning” font_container=”tag:h3|text_align:left” use_theme_fonts=”yes”][vc_column_text]Symbol Based Learning
Version Space Search
Candidate Elimination Algorithm
Unsupervised Learning
Neural Network Based Learning
Perceptron Learning
Backpropagation Learning
Competitive Learning[/vc_column_text][vc_custom_heading text=”MAPPING OF CLOs TO ASSESSMENT MODULES” font_container=”tag:h2|font_size:20px|text_align:left” use_theme_fonts=”yes”][vc_column_text css=”.vc_custom_1667210898484{margin-bottom: 0px !important;}”]
Final Exam |
Assignments |
Surprise Tests/Quizzes |
Project |
Midterm Exam |
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