Description 1

This course enables learning on different graph traversal techniques (BFS & DFS)
along with enhanced search algorithms like A* algorithm. Genetic algorithms are discussed along with Min-Max algorithms.

Expert systems and ANN are also discussed in detail along with Fuzzy logic in SC.

Along with a pdf with important notes and explanations

Modules Covered:

Introduction to AI / SC
Problem solving algorithms
Knowledge, Reasoning and Planning.
Fuzzy Logic.
Artificial Neural Network.
Expert System.

• Lecture1.1
  How to Pass AISC 18 min

How to Pass AISC 1

• Lecture2.1
  How to Pass AISC 18 min

Introduction to Artificial Intelligence(AI) and Soft Computing 5

• Lecture3.1
  Agent and Peas Description 07 min
• Lecture3.2
  Types of Agent 08 min
• Lecture3.3
  Learning Agent 08 min
• Lecture3.4
  Learning and Types of Learning 06 min
• Lecture3.5
  Soft computing vs Hard computing and Supervised learning vs Unsupervised Learning 10 min

Problem Solving 10

• Lecture4.1
  BFS ( Breadth First Search ) Algorithm with solved Example 05 min
• Lecture4.2
  DFS ( Depth First Search ) Algorithm with solved Example 03 min
• Lecture4.3
  IDFS ( Iterative Depth First Search ) Algorithm with solved Example 03 min
• Lecture4.4
  GBFS Solved Example 07 min
• Lecture4.5
  A Star solved Example 13 min
• Lecture4.6
  Hill Climbing 04 min
• Lecture4.7
  Min Max Solved Example 06 min
• Lecture4.8
  Alpha-Beta Pruning Solved Example 13 min
• Lecture4.9
  Genetic Algorithm 05 min
• Lecture4.10
  Genetic Algorithm Max one Problem Solved Example 08 min

Fuzzy Logic 6

• Lecture5.1
  Introduction to Fuzzy Logic 04 min
• Lecture5.2
  Fuzzification and De-Fuzzification 06 min
• Lecture5.3
  Properties and Operation of Crisp and Fuzzy Sets 05 min
• Lecture5.4
  Crisp and Fuzzy Sets and Relations 11 min
• Lecture5.5
  Fuzzy Membership Function 08 min
• Lecture5.6
  Mamdani Fuzzy Model (Fuzzy Controller) with Solved Example 33 min

Knowledge, Reasoning and Planning 6

• Lecture6.1
  Propositional Logic (PL) Introduction 07 min
• Lecture6.2
  PL to CNF conversion With Solved Example 09 min
• Lecture6.3
   First-Order Logic (FOL) Solved Example 05 min
• Lecture6.4
  Resolution Tree Sum Part #1 08 min
• Lecture6.5
  Resolution Tree Sum Part #2 14 min
• Lecture6.6
  Partial Order Planning with Example 11 min

Artificial Neural Network 7

• Lecture7.1
  Introduction to ANN and structure of ANN 06 min
• Lecture7.2
  Mc-Culloch-Pitts Neural Model 03 min
• Lecture7.3
  Neural Network Architecture 05 min
• Lecture7.4
  Perceptron Learning (with solved example) 11 min
• Lecture7.5
  Activation functions in ANN (Discrete and Continuous) 04 min
• Lecture7.6
  Backpropagation Network (with solved example) 19 min
• Lecture7.7
  Self Organizing Maps and KSOMs 10 min

Expert System 4

• Lecture8.1
  Introduction to Hybrid System 04 min
• Lecture8.2
  Neuro-Fuzzy System (Co-Operative and General NFS) 08 min
• Lecture8.3
  Fuzzy Inference System 07 min
• Lecture8.4
  Expert System 10 min

Notes 6

• Lecture9.1
  INTRODUCTION TO ARTIFICIAL INTELLIGENCE(AI) AND SOFT COMPUTING
• Lecture9.2
  PROBLEM SOLVING
• Lecture9.3
  KNOWLEDGE, REASONING AND PLANNING
• Lecture9.4
  FUZZY LOGIC
• Lecture9.5
  ARTIFICIAL NEURAL NETWORK
• Lecture9.6
  EXPERT SYSTEM

Extra Notes 9

• Lecture10.1
  Introduction to Artificial Intelligence and Soft Computing (Module 1 Notes)
• Lecture10.2
  Artificial Intelligence Notes #1
• Lecture10.3
  Artificial Intelligence Notes #2
• Lecture10.4
  Soft Computing Module 4
• Lecture10.5
  Soft Computing Module 5
• Lecture10.6
  Soft Computing Handmade Notes
• Lecture10.7
  Artificial Intelligence and Soft Computing Complete Notes ( Toppers Solution )
• Lecture10.8
  Mobile Communication and Computing Notes ( Toppers Solution )
• Lecture10.9
  Digital Signal Processing Handmade Notes