| Date | Topics for Discussion |
|---|---|
| 02/04 |
Introduction to TopicsClick below to view the slides from our first meeting: 📄 View Presentation (PDF) |
| 02/11 |
Introduction to AI and Machine LearningWe will be following the book: Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville |
| 02/18 |
An Introduction to Machine Learning AlgorithmsSlides presentation of today's session: 📄 View Presentation (PPT) |
| 02/25 |
Worked on Example of Back PropagationReleated material: How to compute gradients with backpropagation for arbitrary loss and activation functions |
| 03/11/25 |
Monte Carlo MethodsChapter 17 of Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville |
| 03/18 | Understanding Backpropagation Through Examples |
| 03/25 | Matrix Calculus |
| 04/01 | Neural NetworkView PDF |
| 04/15 | Robust Optimization |
🔗 Join our discussion on Discord: Graduate Reading Seminar Discord