Introduction to basic methods of Artificial Intelligence (AI) such as problem solving, searching techniques, and knowledge representation. The course also discusses machine learning techniques (supervised and unsupervised learning). This is covered through discussions, projects, and examples, students learn what AI is, some of the major developments in the field, promising directions, and the techniques for making computers exhibit intelligent behavior. Students make use of available tools and explore some areas of applications.
Information Systems (2024)
RUSSELL, GEORGE, Artificial Intelligence: A modern Approach, Pearson
| content serial | Description |
|---|---|
| 1 | Introduction to AI: Definition, History and Goals |
| 2 | Intelligent Agents and Problem Solving |
| 3 | State Space Representation and Search Techniques (Blind search) |
| 4 | Search Techniques (Heuristic Search, …) |
| 5 | A* Algorithm, Admissibility, Monotonicity and Informedness of a heuristic function |
| 6 | Game trees and Alpha Beta Pruning Algorithm |
| 7 | Propositional Logic |
| 8 | First Order Logic |
| 9 | Introduction to Machine Learning - Supervised Learning |
| 10 | Supervised Learning cont. |
| 11 | Perceptron Learning Algorithm |
| 12 | Artificial Neural Networks |
| 13 | Unsupervised Learning (K-means) |
| 14 | New trends in Artificial Intelligence |
| 15 | Course Review and Conclusion |
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