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
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Prag Robotics
Robotics & A.I.
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- 4 stars: 1 votes
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