Artificial Intelligence course focuses on the development of algorithms and statistical models.
Table of Contents
Course Objectives
- To learn the difference between optimal reasoning vs human like reasoning.
- To understand the notions of state space representation, exhaustive search, heuristic search.
- To learn different knowledge representation techniques.
- To understand the applications of AI like Game Playing and Expert Systems.
- To introduce the concept of Machine Learning.
Course Syllabus
UNIT 1
Introduction: History, Intelligent Systems, Foundations of AI, Subareas of AI & Applications.
Intelligent Agents: Agents and Environment, Structure of Agents
Solving Problems by Searching:
Uninformed Search Strategies – Depth-first search – Breadth-first search – Uniform-cost search
Informed (Heuristic) Search Strategies – Heuristic Functions – Greedy best-first search – A* search
UNIT 2
Adversarial Search: Optimal Decisions in Games – MiniMax Algorithm, Alpha-Beta Pruning
Constraint Satisfaction Problems
Minimum Description Length Principle – Gibbs Algorithm – Bayesian Belief Networks – EM Algorithm
UNIT 3
UNIT 4
UNIT 5
Text Books
Saroj Kaushik, Artificial Intelligence, Cengage Learning, 2011.
Reference Books
- Rich, Knight, Nair: Artificial Intelligence, Tata McGraw Hill, 3rd Edition, 2009.
- Eugene Charniak, Introduction to Artificial Intelligence, Pearson, 2007
- Dan W.Patterson, Introduction to Artificial Intelligence and Expert Systems, PHI, 1990.
- George Fluger, Artificial Intelligence, 5th Edition, Pearson.
Online Resources
- http://www.vssut.ac.in/lecture_notes/lecture1428643004.pdf
- http://nptel.ac.in/courses/106105077/
- https://onlinecourses.nptel.ac.in/noc18_cs18/preview
- https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x-4
Course Outcomes
After completion of the course, students will be able to:
- Understand the basics of AI and to formulate efficient problem space and select a search algorithm for a problem.
- Apply AI techniques to solve problems related to Game Playing, Expert Systems.
- Understand and apply Logic programming in problem solving.
- Represent knowledge using appropriate techniques.
- Interpretation of probabilistic and logical reasoning in knowledge base.
- Understand the concepts of machine learning.
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