Schedule



´╗┐Week Lecture Lecture Lab A-Section Homework Midterm Project
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 A-Section 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 A-Section 2: Regularization/Dropout Homework 5
9 Lecture 14: Regularization Lecture 15: Optimization Lab 9: Regularization A-Section 3: Neural style transfer learning
10 Lecture 16: CNN Lecture 17: RNN Lab 10: CNN A-Section 4: Examples of ConvNets: LeNet, AlexNet, VGG-15, ResNet and Inception Homework 6 Milestone 2: Lit review, pre-EDA and SOW
11 Lecture 18: RNN Lecture 19: Autoencoders Lab 11: RNN A-Section 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