Foundations Of A.I. – 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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