Artificial Intelligence Principles, and Practices Part I

Name: Artificial Intelligence Principles, and Practices Part I
Rating: 4.4 (281 reviews)

Learn the Foundations and become an AI expert

Created byXavier Chelladurai

Last updated 10/2025

English

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

Course content

10 sections • 45 lectures • 10h 26m total length

Introduction to Artificial Intelligence and Machine Learning8:48
Introduction to Artificial Intelligence
Machine Learning - Introduction8:29
Introduction to Machine Learning
Lab Exercise - Simple Programs to brush up Python skill

What is an Intelligent Agent?11:25
In this lecture we introduce the concept of intelligent agents.
Percept Sequence - Historical Knowledge and Data14:26
Explore how an intelligent agent uses sensors and actuators to perceive environment, record a percept sequence from history, and choose actions via an agent program in state and action spaces.
Intelligent Agent - Performance Metric9:50
Define performance metrics by translating the agent's purpose into measurable criteria. Relate environment state and actions to success with mathematical functions, illustrated by vacuum cleaner and autonomous car examples.
Intelligent Agent Program7:10
Detailed explanation on Agent programs
Lab Exercise - Simple Reflex Agent Program
Introduction to Intelligent Agents21:30
Introduction: Intelligent Agents – Agents and environments - Good behavior
Reading Assignment - Introduction to Intelligent Agents
Nature of Environment27:44
Define the task environment using PEAS—performance measures, environment, actuators, and sensors—for intelligent agents. Examine observability, single versus multiple agents, determinism versus stochasticity, episodic versus sequential, and static versus dynamic contexts.

Problem Solving Agents - The structure10:13
Problem Solving Agent - Structure
Algorithm for Problem Solving Agent7:49
Algorithm for Problem Solving Agent. Example.
Example of Problem Solving Agent- The Toy Problem13:52
Example of Problem Solving Agent- The Toy Problem
Example of Problem Solving Agent - The 8-Puzzle problem11:06
The 8-Puzzle Problem
Lab Exercise - Problem Solving Agent for 8-Puzzle Game
Example of Problem Solving Agent - The 8 - Queens problem5:52
Example of Problem Solving Agent - The 8 - Queens problem
Four Coloring problem - Solution for Exercise problem - University Question11:33
Example of Problem Solving Agent - Four Color problem
Auto car route among obstacles - Solution for Exercise problem - University Qn6:14
Auto car route among obstacles - Solution for Exercise problem - University Qn
The Wumpus World Problem14:26
The Wumpus World Problem - Example of Problem Solving Agent

Acting Under Uncertainty11:55
Intelligent Agents acting under uncertainty
Introduction to Probability16:23
Foundations and introduction to the concept of probability
Conditional Probability10:33
Conditional Probability
Introduction to Bayes' Rule and its applications13:27
Introduction to Bayes's Rule and its applications
Inference from Full Joint Distributions14:37
Inference from Full Joint Distributions
Bayes Theorem15:52
Inference using full joint distributions; –Independence; Bayes’ rule and its use; –The Wumpus world revisited
Lab Exercise - Python Program for Bayes Theorem and its applications
Exercise Problem Solution - University Question7:09
Bayes Algorithm - Problems from Exercises

Uninformed Search Strategies24:00
Uninformed Search Strategies
Breadth First Search - High Level Algorithm24:46
Breadth First Search - High Level Algorithm
Understanding BFS10:41
Explore the breadth first search by starting at a node, visiting successive distance layers with a queue, and checking neighbors until a solution is found.
Data Structures and Algorithms for BFS10:38
Learn to implement breadth-first search in Python using a queue, an explored set, and a BFS tree with a parent relation and levels, starting from zero with an adjacency matrix.
BFS Detailed Algorithm Walkthrough11:58
Explore a detailed BFS walkthrough from node zero, using a queue, explored set, and parent tracking to update levels while visiting adjacent vertices.
Python Program for Breadth First Search14:00
Implement breadth first search in Python with an adjacency matrix, using a BFS function that tracks path costs, explored nodes, a queue, and a BFS tree with parents.
Lab Exercise Python Program for Breadth First Search
Uniform Cost Search16:30
Uniform Cost Search

Uniform Cost Search - High Level Algorithm18:24
Uniform Cost Search
Uniform Cost Search Walkthrough of Algorithm with an example Part 117:35
Explore uniform cost search on a weighted graph using a distance matrix and a priority queue to expand neighbors and update path costs toward a solution.
Uniform Cost Search - Walkthrough Part 215:43
Walks through a uniform cost search walkthrough, detailing iterations, frontier and queue operations, and parent relationships, and concludes with optimality and time-space complexity O(b^D).
Lab Exercise - Python Program for Uniform Cost Search

Depth First Search Algorithm23:11
Compare breadth-first and depth-first search, then implement depth-first search using a stack or recursion, explore neighbors, and backtrack while studying limited depth, iterative deepening, and bidirectional variations.
Depth First Search - Walkthrough with Example11:43
Execute depth-first search on a 15-node graph using an adjacency matrix. See how a stack governs backtracking and DFS through each neighbor to build the DFS tree.
Depth First Search - Algorithm - Develop Python Program10:47
Depth First Search - Algorithm Walkthrough with Example
Lab Exercise - Python Program for Depth First Search

Informed (Heuristic) Search Strategies10:41
Explore informed search strategies that use domain knowledge and heuristics to improve efficiency, with best first search and a heuristic function, illustrated by eight queens and Arad to Bucharest.
A* Search Algorithm with example- Heuristic Fn Minimize total Estimated cost15:43
A Star Search Algorithm
A* Search Algorithm- Heuristic Fn -Admissibility and Consistency15:45
Learn how A* search uses g(x) and h(x) to find optimal solutions with admissible and consistent heuristics. Compare tree and graph search and discuss contour and heuristic error.
Memory Bound Search Strategies9:02
Explore memory-bounded search strategies that balance memory usage by managing the frontier, in algorithms like breadth-first and depth-first search, and A* variants such as iddfs, rbfs, ma*, and sma*.

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

Artificial Intelligence Principles, and Practices Part I

What you'll learn

Explore related topics

Course content

Introduction to Artificial Intelligence2 lectures • 17min

Introduction - Intelligent Agents6 lectures • 1hr 32min

Structure of an Intelligent Agents1 lecture • 23min

Problem Solving Agents8 lectures • 1hr 21min

Agents acting under uncertainity - Bayes Theorem7 lectures • 1hr 30min

Uninformed Intelligent Search Strategies - Breadth First Search7 lectures • 1hr 53min

Uninformed Search Techniques - Uniform Cost Search3 lectures • 52min

Uninformed Search Strategies - Depth First Search3 lectures • 46min

Informed Search Strategies - Heuristic Functions4 lectures • 51min

Local Search Strategies4 lectures • 1hr 1min

Requirements

Description

Who this course is for: