Date Topic covered Assignments
     
23-Aug Class syllabus
Introduction to Artificial Intelligence (AI)
Introduction to Machine Learning (ML)
Introduction to Keras/TensorFlow
30-Aug Applied Mathematics (all) Deep Learning, https://en.wikipedia.org/wiki/Deep_learning
Introduction to Linear Algebra (all) Unstructured Data, http://en.wikipedia.org/wiki/Unstructured_data
Introduction to Probability (all) Deep Learning, Chapters 1-4
(optional) Artificial Neural Networks, https://en.wikipedia.org/wiki/Artificial_neural_network
(optional) Keras docs, https://keras.io/
6-Sep Information Theory & Machine Learning Fundamentals (all) Deep Learning, Chapters 4-5
Introduction to Information Theory
Introduction to Machine Learning (Part 1)
Assignment 1
13-Sep Introduction to Machine Learning (Part 2) (all) Deep Learning, Chapter 5-6
The Multilayer Perceptron & Feedforward Networks (optional) "Learning Representations by Back-Propagating Errors" (Nature), https://www.nature.com/nature/journal/v323/n6088/abs/323533a0.html
Architecture & Gradient-Based Learning
Back-Propagation of Errors
20-Sep Combatting Overparametrization:  Regularization (all) Deep Learning, Chapter 7
Prior Beliefs:  Statistical & Structural Forms
Tricks: Ensembling & Adversarial Training
27-Sep Parameter Optimization (all) Deep Learning, Chapter 8
29-Sep Assignment 1 Due
4-Oct Team Presentations: Project Proposal
11-Oct Convolutional Networks (all) Deep Learning, Chapter 9
18-Oct Optimization (all) Deep Learning, Chapter 8
Assignment 2
25-Oct Modeling Sequences: Recurrence and Recursion (all) Deep Learning, Chapter 10
Special Topic: Neural Language Modeling
1-Nov Practical Methodology (all) Deep Learning, Chapter 11
(optional) Neural Networks: Tricks of the Trade, 2nd Edition -- "Practical Recommendations for Gradient-Based Training of Deep Architectures", https://link.springer.com/chapter/10.1007/978-3-642-35289-8_26
8-Nov Neural Network Applications (all) Deep Learning, Chapter 12
Assignment 2 Due
15-Nov Representation Learning (all) Deep Learning, Chapter 14
(optional) Deep Learning, Chapter 13 (Linear Factor Models)
(all) Deep Learning, Chapters 15
22-Nov Thanksgiving break, no classes
29-Nov Team Presentations 10,1,7,4,11,6
6-Dec Team Presentations 2,9,12,5,3,8,13
12-Dec Project Reports Due ACM paper format 9pt single space double sided.