- Explain the basic mathematical foundation for machine learning techniques.
- Identify the main Deep Learning algorithms and their applicability in Machine Learning tasks.
- Explain details of layered-architectures and their learning algorithms.
- Explain how real-world problems are modelled and solved by Deep Learning techniques.
- Elaborate on the strengths of the state-of- the-art of Deep Learning techniques and the challenges that remain.
- Develop programs using a Deep Learning toolbox.
- Apply metrics for evaluating outcomes of Deep Learning program and analyzing results.
Computer Science - 132 CRs
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| content serial | Description |
|---|
| 1 | Why go deep? |
| 2 | ANN |
| 3 | CNNs for classification |
| 4 | CNNs for semantic segmentation |
| 5 | Recurrent Neural Networks |
| 6 | Hyper-parameters |
| 7 | Boltzmann machines |
| 8 | Restricted Boltzmann machines |
| 9 | Autoencoders |
| 10 | Basics of GANs |
| 11 | Performance measures |
| 12 | Performance analysis |
| 13 | Deep models and reinforcement learning |
| 14 | Explainable AI basics |
| 15 | Revision |
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