The course covers various methods within artificial intelligence (AI) and machine learning (ML), and their applications. Algorithms for supervised and unsupervised learning are included with data examples. The course contains theory components and principles that underlie advanced machine learning algorithms. Practice components are provided to relate theoretical principles with practical implementation.
Bachelor of Computer Science - 144 CRs
Russell S. and Norvig P., Artificial Intelligence: A modern Approach, Prentice-Hall
| content serial | Description |
|---|
| 1 | Introduction |
| 2 | Genetic Algorithms |
| 3 | Genetic Algorithms Applications |
| 4 | Partitioning Clustering Techniques Part I |
| 5 | Hierarchical Clustering Techniques Part I |
| 6 | Hierarchical Clustering Techniques Part II |
| 7 | 7th week exam |
| 8 | Density Based Clustering |
| 9 | Applications of Data Clustering |
| 10 | K Nearest Neighbour Algorithm |
| 11 | Recommendation Engines |
| 12 | 12th week exam |
| 13 | Decision Trees |
| 14 | Applications of Decision Trees |
| 15 | Revision |
| 16 | Final exam |
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