1 
Lecture 0: Introduction 
Lecture 1: Smoothing and Additive 1 
Lab 1: Intro to R 

Homework 0 


2 
Lecture 2: Smoothing and Additive 2 
Lecture 3: Smoothing and Additive 3 
Lab 2: Smoothing/GAM 

Homework 1 


3 
Lecture 4: Unsupervised learning/clustering 1 
Lecture 5: Unsupervised learning/clustering 2 
Lab 3: Unsupervised Learning 1 

Homework 2 


4 
Lecture 6: Unsupervised learning/clustering 3 
Lecture 7: Bayesian 1 
Lab 4: Unsupervised Learning 2 




5 
No Class 
Lecture 8: Bayesian  Basic models, computation 2 
Lab 5: Bayes/Stan 

Homework 3 


6 
Lecture 9: Bayesian  hierarchical models/LDA 3 
Lecture 10: Scalability: concepts up/out 
Lab 6: Bayes/LDA 
ASection 1: Relation with SVM and Logistic Regression 


Milestone 1: Group formation and signup 
7 
Lecture 11: Nueral Net Basics & Math 
No Class 
Lab 7: Scalability xPUs, cloud 

Homework 4 
Midterm 

Spring Break 







8 
Lecture 12: Deep Feed Forward 1 
Lecture 13: Deep Feed Forward 2 
Lab 8: Intro to Keras 
ASection 2: Regularization/Dropout 
Homework 5 


9 
Lecture 14: Regularization 
Lecture 15: Optimization 
Lab 9: Regularization 
ASection 3: Neural style transfer learning 



10 
Lecture 16: CNN 
Lecture 17: RNN 
Lab 10: CNN 
ASection 4: Examples of ConvNets: LeNet, AlexNet, VGG15, ResNet and Inception 
Homework 6 

Milestone 2: Lit review, preEDA and SOW 
11 
Lecture 18: RNN 
Lecture 19: Autoencoders 
Lab 11: RNN 
ASection 5: Deep Gans 



12 
Lecture 20: Generative Models and GANS 
Lecture 21: Databases (SQL, NoSQL) 




Milestone 3: EDA, Baseline analysis 
13 
Lecture 22: Guest Lecture  Aparna Kumar, Spotify 
Lecture 23: Adversarial Examples 





14 
Reading Week 





Milestone 4: Final Project Due 