CS607 : Artificial Intelligence

Course Overview

Course Synopsis

Artificial Intelligence is the study of the science of making intelligent machines, especially intelligent computer programs. In this field we try to understand human intelligence and after it we use computers to adapt (implement using computer programs) this intelligence. This subject contains concepts from many other subjects of computer science and it uses these concepts to give practical solutions for the benefit of human beings.

Course Learning Outcomes

After completing this course you should be able to:

  • Define that what is artificial intelligence and why it is important.
  • Analyze and apply any search strategy over the given problem.
  • Learn about some successful application of artificial intelligence.
  • Characterize and classify expert systems.
  • Design expert system.
  • Use CLIPS software for expert system development.
  • Examine fuzzy systems.
  • Research machine learning and planning.


Course Calendar

TopicLectureResourcePage
Introduction1Handouts4-7
Formal Definitions for Artificial Intelligence2Handouts7-9
History and Evolution of Artificial Intelligence3Handouts9-13
Problem Solving4Handouts15-16
Problem Representation4Handouts16-17
Components of Problem Solving4Handouts17-18
The Two–One Problem5Handouts18-21
Tree and Graph5Handouts21-23
Search Strategies6Handouts23-25
DFS, BFS and Progressive deepening7Handouts2532
Assignment NO. 1
Heuristically Informed Searches8Handouts32-37
Hill Climbing8Handouts37-39
Beam Search8Handouts39-43
Best First Search9Handouts43-47
Branch and Bound9Handouts47-56
A* Procedure9Handouts56-62
Adversarial Search10Handouts62-63
Alpha Beta Pruning10Handouts63-76
Genetic Algorithm11Handouts76-77
Genetic Algorithm Problems12Handouts77-82
Eight Queens Problem13Handouts82-89
Knowledge Representation and Reasoning14Handouts89-93
Assignment NO. 2
Formal KR Techniques15Handouts39-102
Reasoning16Handouts102-105
Resolution17Handouts105-111
Expert Systems18Handouts111-113
Quiz no. 1
Role of an Expert System19Handouts113-114
Knowledge Base19Handouts114-117
Expert System and Forward Chaining20Handouts123-126
Expert System and Backward Chaining21Handouts126-132
CLIPS- Introduction22Video Lecture133-136
Mid Term Exams
CLIPS- Integrated Development Environment23Handouts136-142
CLIPS Integrated Development Environment24Video Lecture142-143
Design of expert systems25Handouts143-144
Knowledge Acquisition Techniques25Handouts144-145
Inference networks:25Handouts144-145
Design of expert systems26Video Lecture145-145
Practical Approach for the Design of expert system27Video Lecture145--146
Handling uncertainty with fuzzy systems28Handouts146-146
Fuzzy Sets28Handouts146-148
Fuzzy Logic28Handouts148-148
Boolean versus fuzzy29Handouts148-149
Membership Function29Handouts149-150
Logical and Fuzzy Operators29Handouts150-151
Fuzzy Set Representation30Handouts151-151
Fuzzy Rules30Handouts151-153
Assignment no. 3
Fuzzy Inference System31Handouts153-155
Defuzzify31Handouts155-157
Fuzzy Inference process32Video Lecture157-159
Introduction to learning33Handouts159-160
Three phases in machine learning34Handouts160-161
Training34Handouts161-162
LEARNING34Handouts162-165
Problem and Problem Spaces34Handouts165-166
Instance Space34Handouts166-167
Concept Space34Handouts167-168
Hypothesis Space35Handouts168-170
Version Space and SearchingHandouts170-172
FIND-S36Handouts172-173
Candidate-Elimination Algorithm36Handouts173-176
GDB
Decision trees learning37Handouts176-177
Information Gain38Handouts177-181
Connectionist39Handouts181-184
Quiz no. 2
Linearly Separable Problems39Handouts184-186
Multiple layers of Perceptrons40Handouts186-190
Supervised and Un-supervised Searches41Handouts190-195
Planning42Handouts195-202
Computer Vision43Handouts202-203
Clustering44Handouts204-206
Conclusion45Handouts206-206
Final Term Exams
Note: This is tentative schedule and may subject to change
 
 
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