Schedule:
| Week | Lecture Date | Topic | Course Material |
|---|---|---|---|
| 1 | 15/08 | Introduction & Instructions Assignments Topic Presentations Software and tools |
Slides |
| 2 | 22/08 | Fundamentals of Machine Learning Statistical Inference Classical ML models |
Slides References |
| 3 | 29/08 | Neural Networks I Fundamentals |
Slides References |
| 4 | 05/09 | Neural Networks II Designing Neural Networks |
Slides/notebook References |
| 5 | 12/09 | Neural Networks III Training Practices |
Slides |
| 6 | 26/09 | Supervised Applications Applications in Graphics Transfer Learning |
Slides |
| 7 | 03/10 | Unsupervised Learning Latent Spaces Generative models |
Slides |
| 8 | 10/10 | Generative Modeling Improving Performance |
Slides |
| 9 | 17/10 | Generative Modeling Conditional GANs Image-to-Image translation |
Slides |
| 10 | 24/10 | Introduction to Tensorflow | Colab Notebook CNN example + Lucid References |
Page for the assignments: link