The course covers various methods within artificial intelligence and machine learning, and their applications. Advanced 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 - 132 CRs
Russell S. and Norvig P., Artificial Intelligence: A modern Approach, Prentice-Hall
| content serial | Description |
|---|
| 1 | Introduction |
| 2 | Genetic Algorithms |
| 3 | Genetic Programming Applications |
| 4 | Principal Component Analysis |
| 5 | Supervised Learning (Naive Bayes, K Nearest Neighbors) |
| 6 | Supervised Learning (Decision Trees, Random Forests, SVM) |
| 7 | Unsupervised Learning (Partitioning Clustering) |
| 8 | Unsupervised Learning (Hierarchical Clustering) |
| 9 | Unsupervised Learning (Density Based Clustering) |
| 10 | Recommendation Engines |
| 11 | Introduction to Deep Learning |
| 12 | Convolutional Neural Networks |
| 13 | New Trends in AI |
| 14 | Research Activities |
| 15 | Revision |
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