Lectures

1. Organization (new), Recap ML & DL (video)

2. Advanced Network Architectures (video)

3. Hyperparameter & Architecture Search (video)

4. Adversarial Examples & Adversarial Training (video)

5. Bayesian Deep Learning (video)

6. Generative Models: VAEs & GANs (video)

7. Deep Reinforcement Learning I (video)

8. Deep Reinforcement Learning II (video)

9. Deep Reinforcement Learning III

10. Guest Lecture from DeepMind I

11. Guest Lecture from DeepMind II

12. Guest Lecture from Intrinsic (formerly Google X Robotics)

The password for the slides is communicated in the lecture.

References

References cited in the slides.

Materials

• I. Goodfellow, Y. Bengio and A. Courville. Deep Learning. MIT Press, 2016.

• Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.

• Kevin Murphy. “Machine Learning: A Probabilistic Perspective”, MIT Press 2012


• (R. Sutton and A. Barton. Reinforcement Learning: An Introduction, MIT Press 2018)

Exercise Questions

Questions similar to those you will be asked in the exam. And the samples solutions.