Date Topic covered Assignments
23-Aug Class syllabus
Introduction to Artificial Intelligence (AI) (all) Deep Learning,
Introduction to Machine Learning (ML) (all) Unstructured Data,
Introduction to Keras/TensorFlow (all) Deep Learning, Chapters 1-5
(optional) Artificial Neural Networks,
(optional) Keras docs,
30-Aug Applied Mathematics & Machine Learning Fundamentals (all) Deep Learning, Chapter 6
Introduction to Linear Algebra (optional) "Learning Representations by Back-Propagating Errors" (Nature),
Introduction to Probability & InformationTheory
Introduction to Machine Learning
6-Sep The Multilayer Perceptron & Feedforward Networks (all) Deep Learning, Chapter 7
Architecture & Gradient-Based Learning
Back-Propagation of Errors
13-Sep Combatting Overparametrization:  Regularization (all) Deep Learning, Chapter 8
Prior Beliefs:  Statistical & Structural Forms
Tricks: Ensembling & Adversarial Training
20-Sep Parameter Optimization (all) Deep Learning, Chapter 9
27-Sep Convolutional Networks (all) Deep Learning, Chapter 10
4-Oct Modeling Sequences: Recurrence and Recursion (all) Deep Learning, Chapter 11
Special Topic: Neural Language Modeling (optional) Neural Networks: Tricks of the Trade, 2nd Edition -- "Practical Recommendations for Gradient-Based Training of Deep Architectures",
11-Oct Practical Methodology (all) Deep Learning, Chapter 12
18-Oct Neural Network Applications (all) Deep Learning, Chapter 13-14
25-Oct Linear Factor Models & Autoencoders (all) Deep Learning, Chapters 15
1-Nov Representation Learning (all) Deep Learning, Chapter 16
8-Nov (Deep) Structured Probabilistic Models
15-Nov Special Topic (TBA)
Candidate Topic: Deep Generative Models & Approximate Inference (Reading: Deep Learning, Chapters 19-20)
Candidate Topic: Deep Security: Adversarial Learning
Candidate Topic: Memory Models & Rule Extraction
22-Nov Thanksgiving break, no classes
29-Nov Team Presentaions
6-Dec Team Presentations
13-Dec Team Presentations