Artificial Intelligence 4-WEEK

Artificial Intelligence (AI) is a branch of computer science that aims to create machines capable of performing tasks that normally require human intelligence. These tasks include learning from experience, reasoning, problem-solving, perception, language understanding, and decision-making.

In this course, students will explore the fundamental concepts and techniques of AI, including intelligent agents, search algorithms, knowledge representation, reasoning, planning, and machine learning. The course also covers applications of AI in real-world domains such as robotics, natural language processing, and computer vision.

By the end of the course, students will gain a solid understanding of how AI systems are designed and how they can be applied to solve complex problems in an efficient and intelligent manner.

Course Content

Chapter 1
In this lecture, we explored the basic concept and scope of Artificial Intelligence (AI). AI is the branch of computer science that focuses on creating machines capable of performing tasks that normally require human intelligence—such as learning, reasoning, problem-solving, perception, and decision-making.

Chapter 1

05:23
AI Lecture 1 Quiz

Chapter 2
Chapter 2 introduces the concept of intelligent agents, which are systems that perceive their environment and take actions to achieve specific goals. An agent uses sensors to receive inputs and actuators to perform actions. The chapter explains different types of agents such as: Simple Reflex Agents – act only on current perception Model-Based Agents – use internal memory Goal-Based Agents – choose actions that achieve goals Utility-Based Agents – select the most beneficial action Learning Agents – improve performance over time

Chapter 3
Chapter 3 focuses on how AI systems solve problems by searching through possible actions to reach a desired goal. A problem is defined by an initial state, actions, transition model, and goal state. AI uses search strategies to explore different paths and find solutions efficiently. The chapter explains two main types of search: Uninformed (Blind) Search – no extra information is used. Examples: Breadth-First Search (BFS), Depth-First Search (DFS), Uniform Cost Search. Informed (Heuristic) Search – uses heuristics to guide the search. Examples: Greedy Best-First Search, A*.

Chapter 4
Chapter 4 explains how AI systems store, organize, and use knowledge to make intelligent decisions. Knowledge Representation (KR) provides ways to describe facts about the world using logic, symbols, and structured formats. The chapter introduces propositional logic and first-order logic (FOL) as formal languages for representing knowledge. AI uses inference rules such as Modus Ponens, resolution, and unification to derive new conclusions from known facts.

Chapter 5
Chapter 5 introduces planning, a key AI process where an agent decides a sequence of actions to achieve a goal. Unlike simple search, planning uses structured representations of states, actions, and goals to create efficient action sequences. The chapter explains types of planning, such as: State-space planning – searching through states. Plan-space (Partial Order) planning – creating flexible plans where actions don’t need a fixed order. Goal stack planning – breaking goals into smaller subgoals.

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A course by

Shankar kumar

AI Engineer

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Price:
Original price was: ₹980.00.Current price is: ₹249.00.
Instructor:Shankar kumar
Duration: 15 hours
Lessons:6
Students:4
Level:All Levels

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