Deep Learning - Lectures
The course comprises 10 lectures.
| # | Lecture | Lecturer | Chapter | Slides |
|---|---|---|---|---|
| 1. | Introduction, ML basics, linear models | NW | [GBC] 1, 5, [LWLS] 2.1-2.3, 3.1-3.2.2 | Le1 |
| 2. | Feed forward neural networks | NW | [GBC] 6.1-6.4, [LWLS] 7.1-7.2, 3.2.3 | Le2 |
| 3. | Optimization: Stochastic gradient and backpropagation | TS | [GBC] 8.1-8.3,6.5, [LWLS] 7.4 | Le3 |
| 4. | Convolutional neural networks 1 | JL | [GBC] 9, [LWLS] 7.3 | Le4 |
| 5. | Convolutional neural networks 2 | JL | [GBC] 9 | Le5 |
| 6. | Over-/underfitting, bias-variance, regularization | NW | [LWLS] 5, 7.4.4, [GBC] 7 | Le6 |
| 7. | Practical methodology and batch normalization | NW | [GBC] 8.7.1, 11 | Le7 |
| 8. | Variational inference | |||
| 9. | Variational autoencoders | |||
| 10. | Project proposal presentation |
The recommended book for the course is
- [GBC] Ian Goodfellow, Yoshua Bengio and Aaron Courville Deep Learning
, MIT Press, 2016.
We will not follow [GBC] strictly and we do not cover all aspects of the suggested chapters in the lecture. For some lectures in the course, the following lecture notes contain the covered material in a more condensed format.
- [LWLS] Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön Supervised machine learning
Another great resource is
- [N] Michael A. Nielson Neural Networks and Deep Learning Determiniation Press, 2015.
which is a bit more hands-on in comparison to [GBC] but does not cover as much and is lacking some details.
For lecture 8 and 9 some additional resources will be used
- [B] Christopher M. Bishop. Pattern Recognition and Machine Learning, Springer, 2006.
- Diederik P. Kingma, Max Welling: Auto-Encoding Variational Bayes. ICLR 2014
NW = Niklas Wahlström
TS = Thomas Schön
JL = Joakim Lindblad