Date 
Topic covered 
Assignments 












23Aug 
Class syllabus 



Introduction to Artificial Intelligence (AI) 
(all) Deep Learning,
https://en.wikipedia.org/wiki/Deep_learning 


Introduction to Machine Learning (ML) 
(all) Unstructured Data,
http://en.wikipedia.org/wiki/Unstructured_data 


Introduction to Keras/TensorFlow 
(all) Deep Learning, Chapters 15 



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



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





30Aug 
Applied Mathematics & Machine Learning Fundamentals 
(all) Deep Learning, Chapter 6 


Introduction to Linear Algebra 
(optional) "Learning
Representations by BackPropagating Errors" (Nature),
https://www.nature.com/nature/journal/v323/n6088/abs/323533a0.html 


Introduction to Probability & InformationTheory 



Introduction to Machine Learning 






6Sep 
The Multilayer Perceptron & Feedforward Networks 
(all) Deep Learning, Chapter 7 


Architecture & GradientBased Learning 



BackPropagation of Errors 






13Sep 
Combatting Overparametrization: Regularization 
(all) Deep Learning, Chapter 8 


Prior Beliefs:
Statistical & Structural Forms 



Tricks: Ensembling & Adversarial Training 






20Sep 
Parameter Optimization 
(all) Deep Learning, Chapter 9 









27Sep 
Convolutional Networks 
(all) Deep Learning, Chapter 10 





4Oct 
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 GradientBased Training of
Deep Architectures",
https://link.springer.com/chapter/10.1007/9783642352898_26 




11Oct 
Practical Methodology 
(all) Deep Learning, Chapter 12 





18Oct 
Neural Network Applications 
(all) Deep Learning, Chapter 1314 





25Oct 
Linear Factor Models & Autoencoders 
(all) Deep Learning, Chapters 15 





1Nov 
Representation Learning 
(all) Deep Learning, Chapter 16 





8Nov 
(Deep) Structured Probabilistic Models 





15Nov 
Special Topic (TBA) 



Candidate Topic: Deep
Generative Models & Approximate Inference (Reading: Deep
Learning, Chapters 1920) 


Candidate Topic: Deep Security: Adversarial Learning 



Candidate Topic: Memory Models & Rule Extraction 






22Nov 
Thanksgiving break, no classes 






29Nov 
Team Presentaions 






6Dec 
Team Presentations 






13Dec 
Team Presentations 



















































































































































