Schedule

* = optional reading

Date	Topic	Readings / Videos	Assignments
26 Oct		* Deep Learning Book - Part I: Applied Math and Machine Learning Basics
02 Nov	MLPs, Gradient Descent & Backpropagation	Deep Learning Book - Chapter 6: Deep Feedforward Networks	First Steps
09 Nov	CNNs	Deep Learning Book - Chapter 9: Convolutional Networks, Sections 9.1 to 9.5	TensorBoard & tf.data
16 Nov	CNNs (cont.)	Deep Learning Book - Chapter 9: Convolutional Networks, remaining sections Reading Assignment	CNNs
23 Nov	RNNs	Deep Learning Book - Chapter 10: Sequence Modeling: Recurrent and Recursive Nets, Sections 10.1 to 10.5	DenseNet
30 Nov	RNNs (cont.)	Deep Learning Book - Chapter 10: Sequence Modeling: Recurrent and Recursive Nets, Sections 10.6, 10.7 and 10.10 to 10.12	RNNs
07 Dec	Attention and Memory	Reading Assignment	More RNNs
14 Dec	Word Embeddings & Language Models	Reading Assignment	Attention-based NMT
21 Dec	Autoencoders & Self-Supervised Representation Learning	Reading Assignment	Autoencoders
04 Jan	Regularization & Optimization	Deep Learning Book - Chapter 7: Regularization Deep Learning Book - Chapter 8: Optimization (except Section 8.6) * optional reading for an introduction to predictive coding as self-supervised learning approach: Relaxing the Constraints on Predictive Coding Models
11 Jan	Practical Methodology	Deep Learning Book - Chapter 11: Practical Methodology Practical Methodology Talk by Ian Goodfellow Andrew Ng: Machine Learning Yearning (additional information)	Adversarial Examples
18 Jan	Introspection - Part I	Reading Assignment	Practical Methodology
25 Jan	Introspection - Part 2	Reading Assignment	Introspection Part 1
01 Feb	TBD	TBD	Introspection Part 2