Ai Mastery: From Search Algorithms To Advanced Strategies
Ai Mastery: From Search Algorithms To Advanced Strategies
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English
| 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.
Free search engine download: AI Mastery From Search Algorithms to Advanced Strategies
