Foundations Of A.I.: Search Algorithms

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Foundations Of A.I.: Search Algorithms

Published 12/2023
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.74 GB | Duration: 4h 19m

Search Algorithms

What you'll learn
Definition of A.I.
Definition of A.I.
Search Algorithms
Elements of Graph Search
Optimization

Requirements
Basic Understanding of Programming
Python Fundamentals

Description
This course is designed for all enthusiasts who are interested for a career in Artificial Intelligence. The main objective of this course is to give a solid foundation of the good old Artificial Intelligence concepts which includes the definition of Artificial Intelligence, different schools of Thought, a tinge of Sir Alan Turing's thoughts about Computational Thinking. As we progress into the course, we will try to understand the significance of graphs and how any problem can be represented as a Graph. At the heart of this course is Search Algorithms, we will have a look at methods that allow computers to search for solution in a huge solution space. In that pursuit, we will work with Uninformed Search and Informed Search Algorithms. Informed Search algorithms have their foot print in Robotics, Navigation systems, designing games and many more. Course is incomplete if we leave with informed search, to counter the problems of search algorithms, we will look into local search which will eventually land in Optimization. In local search, we will work with Hill climbing algorithms along with their disadvantages. To sum up, this course gives answers to questions raised by students who want to explore the fundamentals difference between human intelligence and machine intelligence.

Overview
Section 1: About the Program

Lecture 1 Course Introduction

Lecture 2 Course Outline

Section 2: What is Artificial Intelligence?

Lecture 3 Where is A.I. Around Us?

Lecture 4 What is A.I.?

Lecture 5 A.I. Paradigms

Lecture 6 Why A.I. is Important Now?

Lecture 7 Applications of A.I.

Lecture 8 History of A.I.

Lecture 9 How does Human Intelligence Work?

Section 3: Rationality, Agents & Environment

Lecture 10 What is an Agent and Environment?

Lecture 11 What is Rationality and a Rational Agent?

Lecture 12 Factors of Rationality

Lecture 13 Nature of Environments

Lecture 14 Types of Agent Architectures

Lecture 15 Simple Reflex Agent

Lecture 16 Reflex Agent with Internal State

Lecture 17 Goal based Agent

Lecture 18 Utility based Agents

Section 4: Software Installation

Lecture 19 Installing Anaconda Distribution

Lecture 20 Handling Jupyter Notebooks 1

Lecture 21 Handling Jupyter Notebooks 2

Section 5: Python Crash Course

Lecture 22 What is Python?

Lecture 23 Data Types in Python

Lecture 24 Loops-For (Jupyter)

Lecture 25 Loops-While (Jupyter)

Lecture 26 Creating Functions in Python (Jupyter)

Lecture 27 Conditional Statements - If Else (Jupyter)

Section 6: Problem Solving

Lecture 28 What is a Problem?

Lecture 29 Problem Solving Agent

Lecture 30 Search Algorithms

Lecture 31 Introduction to Graph Search

Lecture 32 Elements of Graph Search

Lecture 33 Types of Search Algorithms

Section 7: Search Algorithms-Uninformed Search

Lecture 34 Uninformed Search Algorithms

Lecture 35 Breadth First Search

Lecture 36 Implementation of Breadth First Search in Python

Lecture 37 Depth First Search

Lecture 38 Implementation of Depth First Search in Python

Lecture 39 Depth Limited Search

Lecture 40 Iterative Deepening Search

Lecture 41 Uniform Cost Search

Section 8: Search Algorithms-Informed Search

Lecture 42 Introduction to Informed Search Algorithms

Lecture 43 Best First Search

Lecture 44 Greedy Search Algorithm

Lecture 45 A* Algorithm

Section 9: Local Search

Lecture 46 Introduction to Local Search

Lecture 47 Hill Climbing Method

Lecture 48 Variants of Hill Climbing Method

Lecture 49 Applications of Hill Climbing

Section 10: About the Program

Lecture 50 Course Conclusion

Anyone interested in the field of Artificial Intelligence,Students from Engineering


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