Section 1 : Introduction and Outline

Lecture 1 What's this course all about 3:55
Lecture 2 Where to get the code for this course 5:2

Section 2 : The High-Level Picture

Lecture 3 Real-World Examples of AB Testing 6:46
Lecture 4 What is Bayesian Machine Learning 11:33

Section 3 : Bayes Rule and Probability Review

Lecture 5 Review Section Introduction 1:22
Lecture 6 Probability and Bayes' Rule Review 5:27
Lecture 7 Calculating Probabilities - Practice 10:25
Lecture 8 The Gambler 5:42
Lecture 9 The Monty Hall Problem
Lecture 10 Maximum Likelihood Estimation - Bernoulli 11:43
Lecture 11 Click-Through Rates (CTR) 2:9
Lecture 12 Maximum Likelihood Estimation - Gaussian (pt 1) 10:7
Lecture 13 Maximum Likelihood Estimation - Gaussian (pt 2) 8:41
Lecture 14 CDFs and Percentiles 9:39
Lecture 15 Probability Review in Code 10:25
Lecture 16 Probability Review Section Summary 5:13
Lecture 17 Beginners Fix Your Understanding of Statistics vs Machine Learning 6:47
Lecture 18 Anyone Can Succeed in this Course 11:55
Lecture 19 Suggestion Box 3:4

Section 4 : Traditional AB Testing

Lecture 20 Confidence Intervals (pt 1) - Intuition 5:9
Lecture 21 Confidence Intervals (pt 2) - Beginner Level 4:45
Lecture 22 Confidence Intervals (pt 3) - Intermediate Level 10:25
Lecture 23 Confidence Intervals (pt 4) - Intermediate Level 11:42
Lecture 24 Confidence Intervals (pt 5) - Intermediate Level 10:8
Lecture 25 Confidence Intervals Code
Lecture 26 Hypothesis Testing - Examples 7:16
Lecture 27 Statistical Significance 5:27
Lecture 28 Hypothesis Testing - The API Approach 9:18
Lecture 29 Hypothesis Testing - Accept Or Reject 2:23
Lecture 30 Hypothesis Testing - Further Examples
Lecture 31 Z-Test Theory (pt 1) 8:47
Lecture 32 Z-Test Theory (pt 2) 8:30
Lecture 33 Z-Test Code (pt 1) 13:2
Lecture 34 Z-Test Code (pt 2) 5:54
Lecture 35 AB Test Exercise 3:55
Lecture 36 Classical AB Testing Section Summary 9:58

Section 5 : Bayesian AB Testing

Lecture 37 Section Introduction The Explore-Exploit Dilemma 10:17
Lecture 38 Applications of the Explore-Exploit Dilemma 7:49
Lecture 39 Epsilon-Greedy Theory 7:5
Lecture 40 Calculating a Sample Mean (pt 1) 5:56
Lecture 41 Epsilon-Greedy Beginner's Exercise Prompt 5:5
Lecture 42 Designing Your Bandit Program 4:9
Lecture 43 Epsilon-Greedy in Code 7:12
Lecture 44 Comparing Different Epsilons 6:3
Lecture 45 Optimistic Initial Values Theory 5:40
Lecture 46 Optimistic Initial Values Beginner's Exercise Prompt 2:26
Lecture 47 Optimistic Initial Values Code 4:18
Lecture 48 UCB1 Theory 14:33
Lecture 49 UCB1 Beginner's Exercise Prompt 2:14
Lecture 50 UCB1 Code 3:28
Lecture 51 Bayesian Bandits Thompson Sampling Theory (pt 1) 12:43
Lecture 52 Bayesian Bandits Thompson Sampling Theory (pt 2) 2:50
Lecture 53 Thompson Sampling Beginner's Exercise Prompt 17:35
Lecture 54 Thompson Sampling Code
Lecture 55 Thompson Sampling With Gaussian Reward Theory 11:24
Lecture 56 Thompson Sampling With Gaussian Reward Code 6:18
Lecture 57 Why don't we just use a library 5:40
Lecture 58 Nonstationary Bandits 7:12
Lecture 59 Bandit Summary, Real Data, and Online Learning 6:11
Lecture 60 (Optional) Alternative Bandit Designs 10:5

Section 6 : Bayesian AB Testing Extension

Lecture 61 More about the Explore-Exploit Dilemma 7:39
Lecture 62 About Proctor Testing Pdf
Lecture 63 Adaptive Ad Server Exercise 5:38

Section 7 : Practice Makes Perfect

Lecture 64 Intro to Exercises on Conjugate Priors 6:4
Lecture 65 Exercise Die Roll 2:38
Lecture 66 The most important quiz of all - Obtaining an infinite amount of practice 9:27

Section 8 : Setting Up Your Environment (FAQ by Student Request)

Lecture 67 Windows-Focused Environment Setup 2018 20:20
Lecture 68 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:33

Section 9 : Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 69 How to Code by Yourself (part 1) 15:54
Lecture 70 How to Code by Yourself (part 2) 9:23
Lecture 71 Proof that using Jupyter Notebook is the same as not using it 12:29
Lecture 72 Python 2 vs Python 3 4:28

Section 10 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 73 How to Succeed in this Course (Long Version)
Lecture 74 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 22:4
Lecture 75 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:19
Lecture 76 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:7

Section 11 : Appendix FAQ Finale

Lecture 77 What is the Appendix 2:48