Artificial Intelligence with Core Concepts and Fundamentals

Name: Artificial Intelligence with Core Concepts and Fundamentals
Rating: 5.0 (2 reviews)

A Step-by-Step Guide to Core Concepts and Fundamentals

Created byR Thamizhamuthu, Maniraj S P

Last updated 7/2026

English

What you'll learn

Formulate a problem as a state space search method and its solution using various AI techniques
Apply appropriate searching techniques to solve a real-world problem
Develop various game playing strategies to solve real world adversarial search problems
Represent various knowledge representation techniques to solve complex AI problems
Design an expert system to implement advance techniques in Artificial Intelligence

Course content

5 sections • 31 lectures • 17h 21m total length

Alpha Beta Pruning31:06
Uninformed search33:15
Travelling Sales Man Problem39:57
Explore the traveling salesman problem in artificial intelligence, focusing on its core concepts and the fundamentals of this classic challenge.
Travelling salesman problem36:02
BFS and DFS_Step_by_Step_process51:00
Explore the step-by-step process of breadth-first search and depth-first search within artificial intelligence contexts, comparing traversal strategies and practical applications in problem solving.
Uniform Cost search and depth limited search56:17
Depth Limited Search and Bi directional search33:34
Explore depth-limited search and bi-directional search to understand constrained pathfinding and efficient problem solving in AI core concepts.
Best first search43:16
Delve into best first search within artificial intelligence, covering core concepts and fundamentals of AI and how this search strategy guides problem solving.

Forward Reasoning (Forward Chaining)11:38
Forward chaining is an inference technique used in Artificial Intelligence where reasoning starts from known facts and applies rules repeatedly to derive new facts until a goal is reached. It is a data-driven approach that moves step-by-step from given information toward conclusions.
Unification and Unification Algorithm10:08
Unification is a process in First Order Logic (FOL).
It makes two logical expressions identical.
Achieved by substituting variables with constants or terms.
Widely used in Artificial Intelligence and automated reasoning.
First Order Logic6:42
First Order Logic (FOL), also called Predicate Logic, is a formal system used to represent and reason about objects, their properties, and relationships between them. It is more expressive than propositional logic because it can talk about individual objects and quantities
Resolution12:10
Resolution is a logical method used to validate statements by showing that assuming the negation of a statement leads to a contradiction. In simple terms, instead of proving something directly, we assume it is false and check whether it creates inconsistency.
Resolution Part II example12:13
the clear step-by-step description of the resolution graph- Negate Likes(John, Peanut). Resolve with rules to derive ¬Food(Peanut), then infer Killed(Anil, Peanut). Contradiction with ¬Killed(Anil, Peanut) proves the statement true using resolution method.
The Wumpus world27:31
The objective of the Wumpus World is for an intelligent agent to use percepts and logical reasoning to find the gold, avoid dangers such as pits and the Wumpus, and return safely to the starting position while maximizing its performance score.
Unification in First Order Logic – 110:08
Unification in first order logic makes two expressions identical by substitutions, ensuring predicate symbols match and argument counts align, while standardizing techniques prevent variable clashes.
Knowledge Representation and Knowledge based agent55:37
Explore knowledge representation techniques and the design of knowledge-based agents to solve problems in artificial intelligence, grounded in core concepts and fundamentals.
Knowledge representation using rules, semantic nets and frames51:22
Explore knowledge representation using rules, semantic nets, and frames to model intelligent behavior. Learn how these core representations support AI reasoning and problem solving.

Mini max algorithm16:42
Explore adversarial search with the minimax algorithm to choose optimal moves in two-player games. Learn how backtracking and alpha beta pruning optimize decisions in games like chess and tic-tac-toe.
The map coloring problem33:03
Constraint Satisfaction Problem (CSP)13:04
Explore constraint satisfaction problems (CSP) by defining variables, domains, and constraints, and classifying unary, binary, and higher-order constraints; apply to cryptarithm puzzles, map coloring, and timetabling.
Environment and its types31:10
Types of Agent41:31
Agent, Rational Agent, PEAS45:08
Investigate agents and rational agents, and apply the peas framework to analyze performance, environment, actuators, and sensors in artificial intelligence systems.

Planning and Planning problem28:38
Introduction to expert system31:10
Machine learning - Learning concepts33:03
Machine Learning, one of the most important areas of Artificial Intelligence. Machine Learning enables computers to learn from data and improve their performance without being explicitly programmed. It is widely used in applications such as recommendation systems, speech recognition, autonomous vehicles, healthcare, and finance.
Learning Method57:47

Requirements

All concepts will be explained step-by-step in a simple manner

Description

This course provides a comprehensive introduction to Artificial Intelligence, covering essential topics such as problem-solving techniques, search algorithms, intelligent agents, and knowledge representation. It is designed to help learners understand how AI systems think, learn, and make decisions. Learners will explore key areas including adversarial search, constraint satisfaction problems, planning strategies, and expert systems in a structured and easy-to-follow manner. The course emphasizes clear explanations and real-world examples to simplify complex concepts and enhance understanding. It also introduces the fundamental principles behind modern AI applications used across industries. By the end of this course, students will have a strong foundation in AI concepts, enabling them to pursue advanced studies, research, or career opportunities in Artificial Intelligence and related domains with confidence.

In addition, the course encourages critical thinking and analytical skills by presenting various AI approaches and their real-life significance. It highlights the evolution of AI and its impact on different sectors such as healthcare, education, and business. Learners will gain a deeper understanding of how intelligent systems are designed and applied, making them better prepared for future advancements in the field, innovation, and emerging technologies across global industries, while developing problem-solving abilities and a strong conceptual understanding essential for academic and professional success.

Who this course is for:

Students aiming to build a strong foundation before advanced AI applications

Artificial Intelligence with Core Concepts and Fundamentals

What you'll learn

Explore related topics

Course content

Basic Introduction to Data Structure and Search Algorithms8 lectures • 5hr 24min

Knowledge Representation9 lectures • 3hr 17min

Adversarial Search Problems and Intelligent Agent6 lectures • 3hr 1min

Planning and Expert System4 lectures • 2hr 31min

Introduction to AI4 lectures • 3hr 9min

Requirements

Description

Who this course is for: