Lectures Syllabus

Date Lecture Teacher Materials Notes / Reading
16.02.2022 1. Introduction to Neural Networks BF slides
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McCulloch-Pitts Neuron
23.02.2022 2. The Power of Neural Networks BF slides
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02.03.2022 3. Backpropagation AS slides
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09.03.2022 4. Stochastic Gradient Descent (SGD) AS slides
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16.03.2022 5. Convolutional Neural Networks AS slides
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23.03.2022 6. Data Augmentation, Weight Initialization, Batch Normalization BF slides
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30.03.2022 7. Regularization Methods for NNs AS slides
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06.04.2022 8. Adaptive SGD Methods AS slides
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13.04.2022 9. Adversarial Patterns. Robust Learning Approaches BF slides
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20.04.2022 10. Learning Representations I: Metric Learning, Word Vectors AS slides
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27.04.2022 11. Learning Representations II: t-SNE, Stochastic EM AS slides
part 1 part 2
04.05.2022 12. Learning Representations III: Variational Autoencoders BF slides
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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 slides
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courses/bev033dle/lectures.txt · Last modified: 2022/05/25 11:39 by shekhole