| Size: 1.40 GB[/center]
| Duration: 4h 19m
Search Algorithms, Adversarial Search Problems, Knowledge Representation, Planning and Expert System

What you'll learn

Formulate a problem in terms of navigating a state space and outline how a range of AI methodologies can be employed to reach a solution.

Utilize relevant search methodologies to address a practical issue in the real world.

Develop a range of gaming strategies aimed at solving practical adversarial search problems in real-world scenarios.

Illustrate different knowledge representation methods for resolving intricate AI challenges.

Create an expert system that employs advanced techniques in Artificial Intelligence.

Requirements

No Pre-requisite and Co- requisite Courses are required

Description

Embark on a thorough exploration of Artificial Intelligence (AI) in this meticulously designed course, suitable for both beginners and intermediate learners. Covering a diverse range of topics, it establishes a strong foundation in AI theory and algorithms while also delving into practical applications.Commencing with an overview of AI and its techniques, participants will delve into problem-solving approaches, encompassing both theoretical concepts and real-world implementations. Dive into engaging toy problems like Tic-tac-toe and the Travelling Salesman Problem to gain hands-on problem-solving experience.Detailed lectures will introduce participants to general search algorithms, both uninformed and informed search methods, and adversarial strategies using game theory, including the Mini Max Algorithm. Additionally, the course explores Constraint Satisfactory Problems (CSP) and the pivotal role of intelligent agents in decision-making processes.Understanding knowledge representation is vital in AI, and this course provides comprehensive coverage. From foundational propositional and predicate logic to advanced techniques like knowledge representation using rules, semantic nets, and frames, participants will gain insight into how AI systems store and process information.As the course progresses, participants will delve into uncertainty in knowledge and reasoning, explore machine learning algorithms, and grasp the fundamentals of expert systems. By course completion, participants will possess a firm understanding of AI theory and practical skills applicable to real-world scenarios.Whether you're a student, professional, or enthusiast, this course empowers you with the knowledge and tools to navigate the dynamic field of Artificial Intelligence confidently. Join us on this educational journey and discover AI's potential to revolutionize industries and shape the future.

Overview

Section 1: Introduction to Artificial Intelligence

Lecture 1 Introduction to AI

Lecture 2 AI techniques

Lecture 3 Problem solving with AI

Lecture 4 AI Models

Lecture 5 Data acquisition and learning aspects in AI

Lecture 6 Problem solving process

Lecture 7 Formulating problems

Lecture 8 Problem types and characteristics and Problem space and search

Lecture 9 Toy Problems-Tic-tac-toe problems

Lecture 10 Missionaries and Cannibals Problem

Lecture 11 Real World Problem–Travelling Salesman Problem

Section 2: Basic Introduction to Data Structure and Search Algorithms

Lecture 12 Basic introduction to trees

Lecture 13 Basic introduction to Graphs

Lecture 14 General Search Algorithms

Lecture 15 Uninformed Search Methods

Lecture 16 Informed search

Section 3: Adversarial Search Problems and Intelligent Agent

Lecture 17 Adversarial Search Methods (Game Theory) Mini Max Algorithm

Lecture 18 Constraint satisfactory problems and CSP as a search problem (Room colouring)

Lecture 19 Intelligent Agent

Section 4: Knowledge Representation

Lecture 20 Knowledge Representation

Lecture 21 Knowledge based agents and The Wumpus world

Lecture 22 Propositional Logic

Lecture 23 Predicate logic

Lecture 24 Unification

Lecture 25 Knowledge representation using rules

Lecture 26 Knowledge representation using semantic nets and Frames

Lecture 27 Uncertain Knowledge and reasoning Methods

Section 5: Planning and Expert System

Lecture 28 Planning - Simple planning agent, Blocks world problem, Mean Ends analysis

Lecture 29 Machine Learning

This course caters to anyone intrigued by Artificial Intelligence, regardless of their level of expertise.






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