Section 1 : Welcome
Lecture 1 Introduction 00:03:06 Duration
Lecture 2 Outline of the course 00:04:37 Duration
Lecture 3 Where to get the code 00:04:56 Duration
Lecture 4 About Proctor Testing

Section 2 : Simple Recommendation Systems
Lecture 1 Section Introduction and Outline 00:04:09 Duration
Lecture 2 Perspective for this Section 00:03:30 Duration
Lecture 3 Basic Intuitions 00:05:05 Duration
Lecture 4 Associations
Lecture 5 Hacker News - Will you be penalized for talking about the NSA
Lecture 6 Reddit - Should censorship based on politics be allowed 00:08:42 Duration
Lecture 7 Problems with Average Rating & Explore vs 00:10:50 Duration
Lecture 8 Problems with Average Rating & Explore vs 00:07:30 Duration
Lecture 9 About Proctor Testing
Lecture 10 Bayesian Approach part 1 (Optional) 00:10:58 Duration
Lecture 11 Bayesian Approach part 2 (Sampling and Ranking) 00:05:44 Duration
Lecture 12 Bayesian Approach part 3 (Gaussian) 00:08:12 Duration
Lecture 13 Bayesian Approach part 4 (Code) 00:12:02 Duration
Lecture 14 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 15 Demographics and Supervised Learning 00:07:12 Duration
Lecture 16 PageRank (part 1) 00:11:02 Duration
Lecture 17 PageRank (part 2) 00:11:44 Duration
Lecture 18 Evaluating a Ranking 00:04:28 Duration
Lecture 19 Section Conclusion 00:04:00 Duration
Lecture 20 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 3 : Collaborative Filtering
Lecture 1 Collaborative Filtering Section Introduction 00:11:27 Duration
Lecture 2 User-User Collaborative Filtering 00:13:40 Duration
Lecture 3 Collaborative Filtering Exercise Prep 00:10:06 Duration
Lecture 4 Data Preprocessing 00:15:17 Duration
Lecture 5 User-User Collaborative Filtering in Code 00:16:06 Duration
Lecture 6 Item-Item Collaborative Filtering 00:09:03 Duration
Lecture 7 Item-Item Collaborative Filtering in Code 00:07:08 Duration
Lecture 8 Collaborative Filtering Section Conclusion 00:05:25 Duration

Section 4 : Matrix Factorization and Deep Learning
Lecture 1 Matrix Factorization Section Introduction 00:03:59 Duration
Lecture 2 Matrix Factorization - First Steps 00:15:16 Duration
Lecture 3 Matrix Factorization - Training 00:08:47 Duration
Lecture 4 Matrix Factorization - Expanding Our Model 00:07:53 Duration
Lecture 5 Matrix Factorization - Regularization 00:06:07 Duration
Lecture 6 Matrix Factorization - Exercise Prompt
Lecture 7 Matrix Factorization in Code 00:06:17 Duration
Lecture 8 Matrix Factorization in Code - Vectorized 00:10:14 Duration
Lecture 9 SVD (Singular Value Decomposition) 00:07:37 Duration
Lecture 10 Probabilistic Matrix Factorization 00:05:57 Duration
Lecture 11 Bayesian Matrix Factorization 00:05:24 Duration
Lecture 12 Matrix Factorization in Keras (Discussion) 00:07:20 Duration
Lecture 13 Matrix Factorization in Keras (Code) 00:07:15 Duration
Lecture 14 Deep Neural Network (Discussion) 00:02:41 Duration
Lecture 15 Deep Neural Network (Code) 00:02:43 Duration
Lecture 16 Residual Learning (Discussion) 00:01:54 Duration
Lecture 17 Residual Learning (Code) 00:01:59 Duration
Lecture 18 Autoencoders (AutoRec) Discussion 00:10:03 Duration
Lecture 19 Autoencoders (AutoRec) Code 00:11:45 Duration

Section 5 : Restricted Boltzmann Machines (RBMs) for Collaborative Filtering
Lecture 1 RBMs for Collaborative Filtering Section Introduction 00:01:59 Duration
Lecture 2 Intro to RBMs 00:08:18 Duration
Lecture 3 Motivation Behind RBMs 00:06:46 Duration
Lecture 4 Intractability 00:03:03 Duration
Lecture 5 Neural Network Equations 00:07:39 Duration
Lecture 6 Training an RBM (part 1) 00:11:28 Duration
Lecture 7 Training an RBM (part 2) 00:06:13 Duration
Lecture 8 Training an RBM (part 3) - Free Energy
Lecture 9 Categorical RBM for Recommender System Ratings 00:11:21 Duration
Lecture 10 RBM Code pt 1 00:07:27 Duration
Lecture 11 RBM Code pt 2 00:04:16 Duration
Lecture 12 RBM Code pt 3 00:11:43 Duration
Lecture 13 Speeding up the RBM Code 00:07:53 Duration

Section 6 : Big Data Matrix Factorization with Spark Cluster on AWS EC2
Lecture 1 Big Data and Spark Section Introduction
Lecture 2 Setting up Spark in your Local Environment 00:07:26 Duration
Lecture 3 Matrix Factorization in Spark 00:10:28 Duration
Lecture 4 Spark Submit 00:06:27 Duration
Lecture 5 Setting up a Spark Cluster on AWS EC2 00:12:29 Duration
Lecture 6 Making Predictions in the Real World 00:02:37 Duration

Section 7 : Basics Review
Lecture 1 (Review) Keras Discussion 00:06:37 Duration
Lecture 2 (Review) Keras Neural Network in Code 00:06:37 Duration
Lecture 3 (Review) Keras Functional API 00:06:37 Duration
Lecture 4 (Review) Confidence Intervals 00:10:02 Duration
Lecture 5 (Review) Gaussian Conjugate Prior 00:05:32 Duration

Section 8 : Appendix
Lecture 1 What is the Appendix 00:02:42 Duration
Lecture 2 Windows-Focused Environment Setup 2018 00:20:09 Duration
Lecture 3 How to How to install Numpy, Theano, Tensorflow, etc 00:17:30 Duration
Lecture 4 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 00:21:53 Duration
Lecture 5 How to Succeed in this Course (Long Version) 00:10:17 Duration
Lecture 6 How to Code by Yourself (part 1) 00:15:42 Duration
Lecture 7 How to Code by Yourself (part 2) 00:09:23 Duration
Lecture 8 Proof that using Jupyter Notebook is the same as not using it 00:12:24 Duration
Lecture 9 What order should I take your courses in (part 1) 00:11:12 Duration
Lecture 10 What order should I take your courses in (part 2) 00:16:07 Duration
Lecture 11 Python 2 vs Python 3 00:04:31 Duration