Artificial Intelligence Principles, and Practices Part I

Artificial Intelligence Principles, and Practices Part I, available at $54.99, has an average rating of 4, with 32 lectures, based on 178 reviews, and has 2213 subscribers.

You will learn about Artificial Intelligence Concepts, Principles a nd practices Introduction: Intelligent Agents – Agents and environments – Good behaviour – The nature of Agents – Intelligent Agents, Problem Solving Agents, Acting under uncertainty – Inference using full joint distributions; –Independence; Bayes’ rule and its use; –The Wumpus world revisited Searching Techniques: Problem-Solving Agents, Well-defined problems and solutions, Formulating problems, Real- world problems. Uninformed Search Strategies, Breadth-first search, Uniform-cost search, Depth-first search, Depth-limited search, Iterative deepening depth-first search, Bidirectional search, Informed (Heuristic) Search Strategies, Greedy best-first search, A* search: Minimizing the total estimated solution cost, Heuristic Functions. The effect of heuristic accuracy on performance. Beyond Classical Search, Local Search Algorithms and Optimization Problems, Genetic Algorithms and its applications This course is ideal for individuals who are B. Sc. Students of Mathematics, Physics, Electronics, Computer Science Students or B. E. and B. Tech, M.C.A., B. C. A Students or IT Professionals who want to upgrade their skills or AI, ML Developers It is particularly useful for B. Sc. Students of Mathematics, Physics, Electronics, Computer Science Students or B. E. and B. Tech, M.C.A., B. C. A Students or IT Professionals who want to upgrade their skills or AI, ML Developers.

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Summary

Title: Artificial Intelligence Principles, and Practices Part I

Price: $54.99

Average Rating: 4

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

Artificial Intelligence Concepts, Principles a nd practices
Introduction: Intelligent Agents – Agents and environments – Good behaviour – The nature of Agents – Intelligent Agents, Problem Solving Agents,
Acting under uncertainty – Inference using full joint distributions; –Independence; Bayes’ rule and its use; –The Wumpus world revisited
Searching Techniques: Problem-Solving Agents, Well-defined problems and solutions, Formulating problems, Real- world problems.
Uninformed Search Strategies, Breadth-first search, Uniform-cost search, Depth-first search, Depth-limited search, Iterative deepening depth-first search,
Bidirectional search, Informed (Heuristic) Search Strategies, Greedy best-first search, A* search: Minimizing the total estimated solution cost,
Heuristic Functions. The effect of heuristic accuracy on performance. Beyond Classical Search, Local Search Algorithms and Optimization Problems,
Genetic Algorithms and its applications

Who Should Attend

B. Sc. Students of Mathematics, Physics, Electronics, Computer Science Students
B. E. and B. Tech, M.C.A., B. C. A Students
IT Professionals who want to upgrade their skills
AI, ML Developers

Target Audiences

B. Sc. Students of Mathematics, Physics, Electronics, Computer Science Students
B. E. and B. Tech, M.C.A., B. C. A Students
IT Professionals who want to upgrade their skills
AI, ML Developers

Introduction to Artificial Intelligence- The fundamental concepts, principles and practices.: Intelligent Agents – Agents and environments – PEAS Performance Parameters, Environment, Actuators, Sensors. Good behavior – The nature of environments – The structure of agents – Problem-Solving agents – How to define a problem? Problem Definition – State Space, Initial State, Goal State, Goal Test, Transition Model, Actions, Sensors. Acting under uncertainty – The 8-Puzzle problem , The 8-Queens problem. The Wumpus World problem-Partially Observable Space – Inference using full joint distributions; –Independence; Bayes’ rule and its use; –The Wumpus world revisited. Searching Techniques: Tree Search Algorithm and Graph Search Algorithm, Redundant path, Loopy Path – Problem-Solving Agents, Well-defined problems and solutions, Formulating problems, Real-world problems. Uninformed Search Strategies, Breadth-first search, Start from Initial State, Choose the data structures Frontier and Explored set. Uniform-cost search with Priority Queue with the cost function, Depth-first search, Last In First Out Queue – Depth-limited search, Iterative deepening depth-first search, Bidirectional search, Informed (Heuristic) Search Strategies, Greedy best-first search, A* search: Minimizing the total estimated solution cost, Heuristic Functions. The effect of heuristic accuracy on performance. Beyond Classical Search, Local Search Algorithms, Hill Climbing Algorithm, Stochastic Hill Climbing Algorithm. Optimization Problems, Local Search in Continuous Spaces, Local Beam Search, Genetic Algorithm, Example of Gentic Algorithm for 8-Queens problem.

Course Curriculum

Chapter 1: Introduction to Artificial Intelligence

Lecture 1: Introduction to Artificial Intelligence and Machine Learning

Lecture 2: Machine Learning – Introduction

Chapter 2: Introduction – Intelligent Agents

Lecture 1: Introduction to Intelligent Agents

Lecture 2: Nature of Environment

Chapter 3: Structure of an Intelligent Agents

Lecture 1: Structure of Intelligent Agent Reflex Agents Model based agents and goal based

Chapter 4: Problem Solving Agents

Lecture 1: Problem Solving Agents – The structure

Lecture 2: Algorithm for Problem Solving Agent

Lecture 3: Example of Problem Solving Agent- The Toy Problem

Lecture 4: Example of Problem Solving Agent – The 8-Puzzle problem

Lecture 5: Example of Problem Solving Agent – The 8 – Queens problem

Lecture 6: Four Coloring problem – Solution for Exercise problem – University Question

Lecture 7: Auto car route among obstacles – Solution for Exercise problem – University Qn

Lecture 8: The Wumpus World Problem

Chapter 5: Agents acting under uncertainity – Bayes Theorem

Lecture 1: Acting Under Uncertainty

Lecture 2: Introduction to Probability

Lecture 3: Conditional Probability

Lecture 4: Introduction to Bayes' Rule and its applications

Lecture 5: Inference from Full Joint Distributions

Lecture 6: Bayes Theorem

Lecture 7: Exercise Problem Solution – University Question

Chapter 6: Uninformed Intelligent Search Strategies

Lecture 1: Uninformed Search Strategies

Lecture 2: Breadth First Search

Lecture 3: Uniform Cost Search

Lecture 4: Depth First Search

Chapter 7: Informed Search Strategies – Heuristic Functions

Lecture 1: Informed (Heuristic) Search Strategies

Lecture 2: A* Search Algorithm with example- Heuristic Fn Minimize total Estimated cost

Lecture 3: A* Search Algorithm- Heuristic Fn -Admissibility and Consistency

Lecture 4: Memory Bound Search Strategies

Chapter 8: Local Search Strategies

Lecture 1: Local Search Strategies

Lecture 2: Simulated Annealing

Lecture 3: Local Beam Search Algorithms

Lecture 4: Genetic Algorithms

Instructors

Xavier Chelladurai
Professor

Rating Distribution

1 stars: 1 votes
2 stars: 4 votes
3 stars: 18 votes
4 stars: 62 votes
5 stars: 93 votes

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