Foundations of A.I.- Search Algorithms

Foundations of A.I.: Search Algorithms, available at $19.99, has an average rating of 4.5, with 50 lectures, 6 quizzes, based on 1 reviews, and has 9 subscribers.

You will learn about Definition of A.I. Definition of A.I. Search Algorithms Elements of Graph Search Optimization This course is ideal for individuals who are Anyone interested in the field of Artificial Intelligence or Students from Engineering It is particularly useful for Anyone interested in the field of Artificial Intelligence or Students from Engineering.

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Summary

Title: Foundations of A.I.: Search Algorithms

Price: $19.99

Average Rating: 4.5

Number of Lectures: 50

Number of Quizzes: 6

Number of Published Lectures: 50

Number of Published Quizzes: 6

Number of Curriculum Items: 56

Number of Published Curriculum Objects: 56

Original Price: ?3,699

Quality Status: approved

Status: Live

What You Will Learn

Definition of A.I.

Definition of A.I.

Search Algorithms

Elements of Graph Search

Optimization

Who Should Attend

Anyone interested in the field of Artificial Intelligence

Students from Engineering

Target Audiences

Anyone interested in the field of Artificial Intelligence

Students from Engineering

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.

Course Curriculum

Chapter 1: About the Program

Lecture 1: Course Introduction

Lecture 2: Course Outline

Chapter 2: What is Artificial Intelligence?

Lecture 1: Where is A.I. Around Us?

Lecture 2: What is A.I.?

Lecture 3: A.I. Paradigms

Lecture 4: Why A.I. is Important Now?

Lecture 5: Applications of A.I.

Lecture 6: History of A.I.

Lecture 7: How does Human Intelligence Work?

Chapter 3: Rationality, Agents & Environment

Lecture 1: What is an Agent and Environment?

Lecture 2: What is Rationality and a Rational Agent?

Lecture 3: Factors of Rationality

Lecture 4: Nature of Environments

Lecture 5: Types of Agent Architectures

Lecture 6: Simple Reflex Agent

Lecture 7: Reflex Agent with Internal State

Lecture 8: Goal based Agent

Lecture 9: Utility based Agents

Chapter 4: Software Installation

Lecture 1: Installing Anaconda Distribution

Lecture 2: Handling Jupyter Notebooks 1

Lecture 3: Handling Jupyter Notebooks 2

Chapter 5: Python Crash Course

Lecture 1: What is Python?

Lecture 2: Data Types in Python

Lecture 3: Loops-For (Jupyter)

Lecture 4: Loops-While (Jupyter)

Lecture 5: Creating Functions in Python (Jupyter)

Lecture 6: Conditional Statements – If Else (Jupyter)

Chapter 6: Problem Solving

Lecture 1: What is a Problem?

Lecture 2: Problem Solving Agent

Lecture 3: Search Algorithms

Lecture 4: Introduction to Graph Search

Lecture 5: Elements of Graph Search

Lecture 6: Types of Search Algorithms

Chapter 7: Search Algorithms-Uninformed Search

Lecture 1: Uninformed Search Algorithms

Lecture 2: Breadth First Search

Lecture 3: Implementation of Breadth First Search in Python

Lecture 4: Depth First Search

Lecture 5: Implementation of Depth First Search in Python

Lecture 6: Depth Limited Search

Lecture 7: Iterative Deepening Search

Lecture 8: Uniform Cost Search

Chapter 8: Search Algorithms-Informed Search

Lecture 1: Introduction to Informed Search Algorithms

Lecture 2: Best First Search

Lecture 3: Greedy Search Algorithm

Lecture 4: A* Algorithm

Chapter 9: Local Search

Lecture 1: Introduction to Local Search

Lecture 2: Hill Climbing Method

Lecture 3: Variants of Hill Climbing Method

Lecture 4: Applications of Hill Climbing

Chapter 10: About the Program

Lecture 1: Course Conclusion

Instructors

Prag Robotics
Robotics & A.I.

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