Date 
Topic covered 
Assignments 












22Aug 
Class syllabus 



Introduction to Artificial Intelligence (AI) 


Introduction to Machine Learning (ML) 


Introduction to Keras/TensorFlow 




29Aug 
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 14 



(optional) Artificial Neural Networks,
https://en.wikipedia.org/wiki/Artificial_neural_network 



(optional) Keras docs, https://keras.io/ 





5Sep 
Information Theory & Machine Learning Fundamentals 
(all) Deep Learning, Chapters 45 


Introduction to Information Theory 


Introduction to Machine Learning (Part 1) 


Assignment 1 



12Sep 
Introduction to Machine Learning (Part 2) 
(all) Deep Learning, Chapter 56 


The Multilayer Perceptron & Feedforward Networks 
(optional) "Learning
Representations by BackPropagating Errors" (Nature),
https://www.nature.com/nature/journal/v323/n6088/abs/323533a0.html 


Architecture & GradientBased Learning 



BackPropagation of Errors 




19Sep 
Combatting Overparametrization: Regularization 
(all) Deep Learning, Chapter 7 


Prior Beliefs:
Statistical & Structural Forms 


Tricks: Ensembling & Adversarial Training 




26Sep 
Parameter Optimization 
(all) Deep Learning, Chapter 8 


Assignment 1 Due 




3Oct 
Team Presentations: Project Proposal 





10Oct 
Convolutional Networks 
(all) Deep Learning, Chapter 9 





17Oct 
Optimization 
(all) Deep Learning, Chapter 8 


Assignment 2 






24Oct 
Modeling Sequences: Recurrence and Recursion 
(all) Deep Learning, Chapter 10 


Special Topic: Neural Language Modeling 






31Oct 
Practical Methodology 
(all) Deep Learning, Chapter 11 



(optional) Neural Networks: Tricks of the Trade,
2nd Edition  "Practical Recommendations for GradientBased Training of
Deep Architectures",
https://link.springer.com/chapter/10.1007/9783642352898_26 




7Nov 
Neural Network Applications 
(all) Deep Learning, Chapter 12 


Assignment 2 Due 




14Nov 
Representation Learning 
(all) Deep Learning, Chapter 14 



(optional) Deep Learning, Chapter 13 (Linear Factor Models) 



(all) Deep Learning, Chapters 15 





21Nov 
Thanksgiving break, no classes 



28Nov 
Team Presentations 






5Dec 
Team Presentations 






10Dec 
Project Reports Due 
2 hard copies  ACM paper format 9pt single space double
sided. 

















































































































































