Artificial Intelligence Concepts, Principles, and Practices

Learn the Foundations and become an AI expert

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 28 lectures (6h 18m) | Size: 8.2 GB

What you’ll 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

Requirements
Basic Mathematics,
Basic programming skill

Description
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.

Who this course is for
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