Artificial Intelligence
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Course Title: Artificial Intelligence
Course No: IT 228
Nature of the Course: Theory + Lab
Semester: 5
Credit Hours: 3
Course Description
Course Objectives
Course Contents
1. Introduction
3 hrs
1.5. Foundations of AI
- Philosophy
- Economics
- Psychology
- Sociology
- Linguistics
- Neuroscience
- Mathematics
- Computer Science
- Control Theory
1.6. AI Ethics and Responsible AI
- Bias and Fairness in AI
- Transparency and Accountability
2.7. Types of Agents
- Simple Reflexive
- Model Based
- Goal Based
- Utility Based
- Learning Agent
2.8. Environment Types
- Deterministic
- Stochastic
- Static
- Dynamic
- Observable
- Semi-observable
- Single Agent
- Multi Agent
3.8. Uninformed Search
- Depth First Search
- Breadth First Search
- Depth Limited Search
- Iterative Deepening Search
- Bidirectional Search
3.9. Informed Search
- Greedy Best first search
- A* search
- Hill Climbing
- Simulated Annealing
4.5. Types of Knowledge Representation Systems
- Semantic Nets
- Frames
- Conceptual Dependencies
- Scripts
- Rule Based Systems (Production System)
4.7. Predicate Logic, Propositional Logic(PL)
- Syntax
- Semantics
- Formal logic-connectives
- truth tables
- tautology
- validity
- well-formed-formula
- Inference using Resolution
4.9. Predicate Logic: FOPL
- Syntax
- Semantics
- Quantification
- Inference with FOPL: By converting into PL (existential and universal instantiation)
- Unification and lifting
- Inference using resolution
4.17. Fuzzy Logic
- Fuzzy Sets
- Membership in Fuzzy Set
- Fuzzy Rule base Systems
5. Machine Learning
12 hrs
5.5. Learning by Genetic Algorithms
- Operators in Genetic Algorithm: Selection, Mutation, Crossover, Fitness Function
- Genetic Algorithm
5.6. Learning with Neural Networks
- Introduction
- Biological Neural Networks Vs. Artificial Neural Networks (ANN)
- Mathematical Model of ANN
- Activation Functions: Linear, Step Sigmoid
- Types of ANN: Feed-forward, Recurrent, Single Layered, Multi-Layered
- Application of Artificial Neural Networks
- Learning by Training ANN
- Hebbian Learning
- Perceptron Learning
- Back-propagation Learning
5.7. Overview of Deep Learning Architectures
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Generative Adversarial Networks (GANs)
6.1. Expert Systems
- Components of Expert System: Knowledge base, inference engine, user interface, working memory
- Development of Expert Systems
6.2. Natural Language Processing
- Natural Language Understanding and Natural Language Generation
- Steps of Natural Language Processing: Lexical Analysis (Segmentation, Morphological Analysis)
- Syntactic Analysis
- Semantic Analysis
- Pragmatic Analysis
- Machine Translation
6.3. Machine Vision Concepts
- Machine vision and its applications
- Components of Machine Vision System
- Object Detection and Recognition
- Image Segmentation
- Explainable AI in Computer Vision
6.4. AI in Healthcare and Bioinformatics
- Applications of AI in Medicine
- Predictive Modeling in Healthcare
Laboratory Works
- 1.Implementation of Intelligent Agents
- 2.Implementation of Search Algorithms
- 3.Implementation of Knowledge Representation Systems
- 4.Implementation of Machine Learning Techniques
- 5.Implementation of Neural Networks
Text Books
- 1.Stuart Russel and Peter Norvig, Artificial Intelligence A Modern Approach, Fourth Edition 2020, Pearson.
- 2.George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Benjamin/Cummings Publication.
Reference Books
- 1.E. Rich, K. Knight, Shivashankar B. Nair, Artificial Intelligence, Tata McGraw Hill.
- 2.D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall.
- 3.P. H. Winston, Artificial Intelligence, Addison Wesley.