===== Lectures Syllabus =====

^ Date ^ Lecture ^ Teacher ^ Materials ^ Notes / Reading ^
| 16.02.2022 ^ 1. Introduction to Neural Networks | BF | {{:courses:bev033dle:dl-neurons-nets.pdf| slides }} \\ [[https://www.youtube.com/watch?v=aCrp5OlHS-I&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=1|record]] |  [[https://towardsdatascience.com/mcculloch-pitts-model-5fdf65ac5dd1| McCulloch-Pitts Neuron ]] |
| 23.02.2022 ^ 2. The Power of Neural Networks | BF | {{:courses:bev033dle:dl-losses-gen-bounds.pdf| slides }} \\ [[https://www.youtube.com/watch?v=0peiQtt06ec&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=2| record]] |  |
| 02.03.2022 ^ 3. Backpropagation | AS | {{ :courses:bev033dle:backprop.pdf | slides }} \\ [[https://www.youtube.com/watch?v=XhduJETbSMk&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=3|record]] |  |
| 09.03.2022 ^ 4. Stochastic Gradient Descent (SGD) | AS | {{ :courses:bev033dle:sgd.pdf | slides}} \\ [[https://www.youtube.com/watch?v=yoyMeJzLlv8&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=4|record]]|  |
| 16.03.2022 ^ 5. Convolutional Neural Networks | AS | {{ :courses:bev033dle:cnn1.pdf | slides }} \\ [[https://www.youtube.com/watch?v=IhE-qEdQuyg&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=5|record]] |  |
| 23.03.2022 ^ 6. Data Augmentation, Weight Initialization, Batch Normalization  | BF | {{ :courses:bev033dle:dl-init-bn-augm.pdf | slides }} \\ [[https://www.youtube.com/watch?v=KIb-HH5aRvE&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=6|record]] |  |
| 30.03.2022 ^ 7. Regularization Methods for NNs  | AS | {{ :courses:bev033dle:regularizers2.pdf | slides}} \\ [[https://www.youtube.com/watch?v=t3WYddgFD0c&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=7|record]] |  |
| 06.04.2022 ^ 8. Adaptive SGD Methods | AS | {{ :courses:bev033dle:adaptive3.pdf | slides}} \\ [[https://www.youtube.com/watch?v=jqXLFSWmLTQ&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=8|record]] |  |
| 13.04.2022 ^ 9. Adversarial Patterns. Robust Learning Approaches | BF | {{ :courses:bev033dle:adversarial.pdf | slides}} \\ [[https://www.youtube.com/watch?v=TbeOisiiV80&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=9|record]] |  |
| 20.04.2022 ^ 10. Learning Representations I: Metric Learning, Word Vectors | AS | {{ :courses:bev033dle:rl-1.pdf | slides}} \\ [[https://www.youtube.com/watch?v=txEHOquNHUw&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=10 | record]] |  |
| 27.04.2022 ^ 11. Learning Representations II: t-SNE, Stochastic EM  | AS | {{ :courses:bev033dle:svi.pdf | slides}} \\ [[https://www.youtube.com/watch?v=XMorjazYJcI&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=11| part 1]] [[https://www.youtube.com/watch?v=oTM0j-xaqSg&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=12| part 2]]  |  |
| 04.05.2022 ^ 12. Learning Representations III: Variational Autoencoders | BF | {{ :courses:bev033dle:vae.pdf |slides}} \\ [[https://www.youtube.com/watch?v=XIjZ8VAir0o&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=13| record ]] | [[https://arxiv.org/abs/1906.02691 | D. Kingma, M. Welling (2019), An Introduction to VAEs ]] |
| 11.05.2022 | --- Rector's day --- |  |  |  |
| 18.05.2022 ^ 13. Recurrent Neural Networks. Recurrent Back-Propagation | BF | {{ :courses:bev033dle:recurrent.pdf |slides}} \\ [[https://www.youtube.com/watch?v=lasIyyX8xK8&list=PLQL6z4JeTTQnlechDaBvSZiCgArZ60kDR&index=14 | record]] |  |