Section 1 : Welcome

Lecture 1 Introduction 3:6
Lecture 2 Outline of the course 4:37
Lecture 3 Where to get the code 4:56
Lecture 4 About Proctor Testing Pdf

Section 2 : Simple Recommendation Systems

Lecture 5 Section Introduction and Outline 4:9
Lecture 6 Perspective for this Section 3:30
Lecture 7 Basic Intuitions 5:5
Lecture 8 Associations
Lecture 9 Hacker News - Will you be penalized for talking about the NSA
Lecture 10 Reddit - Should censorship based on politics be allowed 8:42
Lecture 11 Problems with Average Rating & Explore vs 10:50
Lecture 12 Problems with Average Rating & Explore vs 7:30
Lecture 13 About Proctor Testing Pdf
Lecture 14 Bayesian Approach part 1 (Optional) 10:58
Lecture 15 Bayesian Approach part 2 (Sampling and Ranking) 5:44
Lecture 16 Bayesian Approach part 3 (Gaussian) 8:12
Lecture 17 Bayesian Approach part 4 (Code) 12:2
Lecture 18 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 19 Demographics and Supervised Learning 7:12
Lecture 20 PageRank (part 1) 11:2
Lecture 21 PageRank (part 2) 11:44
Lecture 22 Evaluating a Ranking 4:28
Lecture 23 Section Conclusion 4:0
Lecture 24 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 3 : Collaborative Filtering

Lecture 25 Collaborative Filtering Section Introduction 11:27
Lecture 26 User-User Collaborative Filtering 13:40
Lecture 27 Collaborative Filtering Exercise Prep 10:6
Lecture 28 Data Preprocessing 15:17
Lecture 29 User-User Collaborative Filtering in Code 16:6
Lecture 30 Item-Item Collaborative Filtering 9:3
Lecture 31 Item-Item Collaborative Filtering in Code 7:8
Lecture 32 Collaborative Filtering Section Conclusion 5:25

Section 4 : Matrix Factorization and Deep Learning

Lecture 33 Matrix Factorization Section Introduction 3:59
Lecture 34 Matrix Factorization - First Steps 15:16
Lecture 35 Matrix Factorization - Training 8:47
Lecture 36 Matrix Factorization - Expanding Our Model 7:53
Lecture 37 Matrix Factorization - Regularization 6:7
Lecture 38 Matrix Factorization - Exercise Prompt
Lecture 39 Matrix Factorization in Code 6:17
Lecture 40 Matrix Factorization in Code - Vectorized 10:14
Lecture 41 SVD (Singular Value Decomposition) 7:37
Lecture 42 Probabilistic Matrix Factorization 5:57
Lecture 43 Bayesian Matrix Factorization 5:24
Lecture 44 Matrix Factorization in Keras (Discussion) 7:20
Lecture 45 Matrix Factorization in Keras (Code) 7:15
Lecture 46 Deep Neural Network (Discussion) 2:41
Lecture 47 Deep Neural Network (Code) 2:43
Lecture 48 Residual Learning (Discussion) 1:54
Lecture 49 Residual Learning (Code) 1:59
Lecture 50 Autoencoders (AutoRec) Discussion 10:3
Lecture 51 Autoencoders (AutoRec) Code 11:45

Section 5 : Restricted Boltzmann Machines (RBMs) for Collaborative Filtering

Lecture 52 RBMs for Collaborative Filtering Section Introduction 1:59
Lecture 53 Intro to RBMs 8:18
Lecture 54 Motivation Behind RBMs 6:46
Lecture 55 Intractability 3:3
Lecture 56 Neural Network Equations 7:39
Lecture 57 Training an RBM (part 1) 11:28
Lecture 58 Training an RBM (part 2) 6:13
Lecture 59 Training an RBM (part 3) - Free Energy
Lecture 60 Categorical RBM for Recommender System Ratings 11:21
Lecture 61 RBM Code pt 1 7:27
Lecture 62 RBM Code pt 2 4:16
Lecture 63 RBM Code pt 3 11:43
Lecture 64 Speeding up the RBM Code 7:53

Section 6 : Big Data Matrix Factorization with Spark Cluster on AWS EC2

Lecture 65 Big Data and Spark Section Introduction
Lecture 66 Setting up Spark in your Local Environment 7:26
Lecture 67 Matrix Factorization in Spark 10:28
Lecture 68 Spark Submit 6:27
Lecture 69 Setting up a Spark Cluster on AWS EC2 12:29
Lecture 70 Making Predictions in the Real World 2:37

Section 7 : Basics Review

Lecture 71 (Review) Keras Discussion 6:37
Lecture 72 (Review) Keras Neural Network in Code 6:37
Lecture 73 (Review) Keras Functional API 6:37
Lecture 74 (Review) Confidence Intervals 10:2
Lecture 75 (Review) Gaussian Conjugate Prior 5:32

Section 8 : Appendix

Lecture 76 What is the Appendix 2:42
Lecture 77 Windows-Focused Environment Setup 2018 20:9
Lecture 78 How to How to install Numpy, Theano, Tensorflow, etc 17:30
Lecture 79 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 21:53
Lecture 80 How to Succeed in this Course (Long Version) 10:17
Lecture 81 How to Code by Yourself (part 1) 15:42
Lecture 82 How to Code by Yourself (part 2) 9:23
Lecture 83 Proof that using Jupyter Notebook is the same as not using it 12:24
Lecture 84 What order should I take your courses in (part 1) 11:12
Lecture 85 What order should I take your courses in (part 2) 16:7
Lecture 86 Python 2 vs Python 3 4:31