Date Topic covered Assignments
22-Aug Class syllabus
Introduction to Artificial Intelligence (AI)
Introduction to Machine Learning (ML)
Introduction to Keras/TensorFlow
29-Aug Applied Mathematics (all) Deep Learning,
Introduction to Linear Algebra (all) Unstructured Data,
Introduction to Probability (all) Deep Learning, Chapters 1-4
(optional) Artificial Neural Networks,
(optional) Keras docs,
5-Sep Information Theory & Machine Learning Fundamentals (all) Deep Learning, Chapters 4-5
Introduction to Information Theory
Introduction to Machine Learning (Part 1)
Assignment 1
12-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),
Architecture & Gradient-Based Learning
Back-Propagation of Errors
19-Sep Combatting Overparametrization:  Regularization (all) Deep Learning, Chapter 7
Prior Beliefs:  Statistical & Structural Forms
Tricks: Ensembling & Adversarial Training
26-Sep Parameter Optimization (all) Deep Learning, Chapter 8
Assignment 1 Due
3-Oct Team Presentations: Project Proposal
10-Oct Convolutional Networks (all) Deep Learning, Chapter 9
17-Oct Optimization (all) Deep Learning, Chapter 8
Assignment 2
24-Oct Modeling Sequences: Recurrence and Recursion (all) Deep Learning, Chapter 10
Special Topic: Neural Language Modeling
31-Oct 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",
7-Nov Neural Network Applications (all) Deep Learning, Chapter 12
Assignment 2 Due
14-Nov Representation Learning (all) Deep Learning, Chapter 14
(optional) Deep Learning, Chapter 13 (Linear Factor Models)
(all) Deep Learning, Chapters 15
21-Nov Thanksgiving break, no classes
28-Nov Team Presentations
5-Dec Team Presentations
10-Dec Project Reports Due 2 hard copies - ACM paper format 9pt single space double sided.