BSc CSIT · Semester IV
Artificial Intelligence syllabus
Official TU syllabus for Artificial Intelligence (CSC266): 6 units, 46 topics, 3 credit hours. Every unit links to its notes and solved questions.
1
Introduction
3h · 6 Q- Artificial Intelligence (AI)
- AI Perspectives: acting and thinking humanly, acting and thinking rationally
- History of AI
- Foundations of AI
- Applications of AI
2
Intelligent Agents
4h · 8 Q- Introduction of agents
- Structure of Intelligent agent
- Properties of Intelligent Agents
- Configuration of Agents
- PEAS description of Agents
- Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based
- Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi Agent
3
Problem Solving by Searching
9h · 17 Q- Definition
- Problem as a state space search
- Problem formulation
- Well-defined problems
- Solving Problems by Searching
- Search Strategies
- Performance evaluation of search techniques
- Uninformed Search: Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Bidirectional Search
- Informed Search: Greedy Best first search, A* search, Hill Climbing, Simulated Annealing
- Game playing
- Adversarial search techniques
- Mini-max Search
- Alpha-Beta Pruning
- Constraint Satisfaction Problems
4
Knowledge Representation
14h · 18 Q- Definition and importance of Knowledge
- Issues in Knowledge Representation
- Knowledge Representation Systems
- Properties of Knowledge Representation Systems
- Types of Knowledge Representation Systems: Semantic Nets, Frames, Conceptual Dependencies, Scripts, Rule Based Systems, Propositional Logic, Predicate Logic
- Propositional Logic(PL): Syntax, Semantics, Formal logic-connectives, truth tables, tautology, validity, well-formed-formula, Inference using Resolution, Backward Chaining and Forward Chaining
- Predicate Logic: FOPL, Syntax, Semantics, Quantification, Inference with FOPL: By converting into PL (Existential and universal instantiation), Unification and lifting, Inference using resolution
- Handling Uncertain Knowledge, Radom Variables, Prior and Posterior Probability, Inference using Full Joint Distribution, Bayes' Rule and its use, Bayesian Networks, Reasoning in Belief Networks
- Fuzzy Logic
5
Machine Learning
9h · 12 Q- Introduction to Machine Learning
- Concepts of Learning
- Supervised, Unsupervised and Reinforcement Learning
- Statistical-based Learning: Naive Bayes Model
- Learning by Genetic Algorithm
- Learning with Neural Networks: Introduction, Biological Neural Networks Vs. Artificial Neural Networks (ANN), Mathematical Model of ANN, Types of ANN: Feed-forward, Recurrent, Single Layered, Multi-Layered, Application of Artificial Neural Networks, Learning by Training ANN, Supervised vs. Unsupervised Learning, Hebbian Learning, Perceptron Learning, Back-propagation Learning
6
Applications of AI
6h · 11 Q- Expert Systems
- Development of Expert Systems
- Natural Language Processing: Natural Language Understanding and Natural Language Generation, Steps of Natural Language Processing
- Machine Vision Concepts
- Robotics
Textbooks and references
- Stuart Russel and Peter Norvig, Artificial Intelligence A Modern Approach, Pearson
- E. Rich, K. Knight, Shivashankar B. Nair, Artificial Intelligence, Tata McGraw Hill.
- George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Benjamin/Cummings Publication
- D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall.
- P. H. Winston, Artificial Intelligence, Addison Wesley.
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