Section 1 : Introduction and Outline

Lecture 1 What's this course all about 00:03:55 Duration
Lecture 2 Where to get the code for this course 00:05:02 Duration

Section 2 : The High-Level Picture

Lecture 1 Real-World Examples of AB Testing 00:06:46 Duration
Lecture 2 What is Bayesian Machine Learning 00:11:33 Duration

Section 3 : Bayes Rule and Probability Review

Lecture 1 Review Section Introduction 00:01:22 Duration
Lecture 2 Probability and Bayes' Rule Review 00:05:27 Duration
Lecture 3 Calculating Probabilities - Practice 00:10:25 Duration
Lecture 4 The Gambler 00:05:42 Duration
Lecture 5 The Monty Hall Problem
Lecture 6 Maximum Likelihood Estimation - Bernoulli 00:11:43 Duration
Lecture 7 Click-Through Rates (CTR) 00:02:09 Duration
Lecture 8 Maximum Likelihood Estimation - Gaussian (pt 1) 00:10:07 Duration
Lecture 9 Maximum Likelihood Estimation - Gaussian (pt 2) 00:08:41 Duration
Lecture 10 CDFs and Percentiles 00:09:39 Duration
Lecture 11 Probability Review in Code 00:10:25 Duration
Lecture 12 Probability Review Section Summary 00:05:13 Duration
Lecture 13 Beginners Fix Your Understanding of Statistics vs Machine Learning 00:06:47 Duration
Lecture 14 Anyone Can Succeed in this Course 00:11:55 Duration
Lecture 15 Suggestion Box 00:03:04 Duration

Section 4 : Traditional AB Testing

Lecture 1 Confidence Intervals (pt 1) - Intuition 00:05:09 Duration
Lecture 2 Confidence Intervals (pt 2) - Beginner Level 00:04:45 Duration
Lecture 3 Confidence Intervals (pt 3) - Intermediate Level 00:10:25 Duration
Lecture 4 Confidence Intervals (pt 4) - Intermediate Level 00:11:42 Duration
Lecture 5 Confidence Intervals (pt 5) - Intermediate Level 00:10:08 Duration
Lecture 6 Confidence Intervals Code
Lecture 7 Hypothesis Testing - Examples 00:07:16 Duration
Lecture 8 Statistical Significance 00:05:27 Duration
Lecture 9 Hypothesis Testing - The API Approach 00:09:18 Duration
Lecture 10 Hypothesis Testing - Accept Or Reject 00:02:23 Duration
Lecture 11 Hypothesis Testing - Further Examples
Lecture 12 Z-Test Theory (pt 1) 00:08:47 Duration
Lecture 13 Z-Test Theory (pt 2) 00:08:30 Duration
Lecture 14 Z-Test Code (pt 1) 00:13:02 Duration
Lecture 15 Z-Test Code (pt 2) 00:05:54 Duration
Lecture 16 AB Test Exercise 00:03:55 Duration
Lecture 17 Classical AB Testing Section Summary 00:09:58 Duration

Section 5 : Bayesian AB Testing

Lecture 1 Section Introduction The Explore-Exploit Dilemma 00:10:17 Duration
Lecture 2 Applications of the Explore-Exploit Dilemma 00:07:49 Duration
Lecture 3 Epsilon-Greedy Theory 00:07:05 Duration
Lecture 4 Calculating a Sample Mean (pt 1) 00:05:56 Duration
Lecture 5 Epsilon-Greedy Beginner's Exercise Prompt 00:05:05 Duration
Lecture 6 Designing Your Bandit Program 00:04:09 Duration
Lecture 7 Epsilon-Greedy in Code 00:07:12 Duration
Lecture 8 Comparing Different Epsilons 00:06:03 Duration
Lecture 9 Optimistic Initial Values Theory 00:05:40 Duration
Lecture 10 Optimistic Initial Values Beginner's Exercise Prompt 00:02:26 Duration
Lecture 11 Optimistic Initial Values Code 00:04:18 Duration
Lecture 12 UCB1 Theory 00:14:33 Duration
Lecture 13 UCB1 Beginner's Exercise Prompt 00:02:14 Duration
Lecture 14 UCB1 Code 00:03:28 Duration
Lecture 15 Bayesian Bandits Thompson Sampling Theory (pt 1) 00:12:43 Duration
Lecture 16 Bayesian Bandits Thompson Sampling Theory (pt 2) 00:02:50 Duration
Lecture 17 Thompson Sampling Beginner's Exercise Prompt 00:17:35 Duration
Lecture 18 Thompson Sampling Code
Lecture 19 Thompson Sampling With Gaussian Reward Theory 00:11:24 Duration
Lecture 20 Thompson Sampling With Gaussian Reward Code 00:06:18 Duration
Lecture 21 Why don't we just use a library 00:05:40 Duration
Lecture 22 Nonstationary Bandits 00:07:12 Duration
Lecture 23 Bandit Summary, Real Data, and Online Learning 00:06:11 Duration
Lecture 24 (Optional) Alternative Bandit Designs 00:10:05 Duration

Section 6 : Bayesian AB Testing Extension

Lecture 1 More about the Explore-Exploit Dilemma 00:07:39 Duration
Lecture 2 About Proctor Testing
Lecture 3 Adaptive Ad Server Exercise 00:05:38 Duration

Section 7 : Practice Makes Perfect

Lecture 1 Intro to Exercises on Conjugate Priors 00:06:04 Duration
Lecture 2 Exercise Die Roll 00:02:38 Duration
Lecture 3 The most important quiz of all - Obtaining an infinite amount of practice 00:09:27 Duration

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

Lecture 1 Windows-Focused Environment Setup 2018 00:20:20 Duration
Lecture 2 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 00:17:33 Duration

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

Lecture 1 How to Code by Yourself (part 1) 00:15:54 Duration
Lecture 2 How to Code by Yourself (part 2) 00:09:23 Duration
Lecture 3 Proof that using Jupyter Notebook is the same as not using it 00:12:29 Duration
Lecture 4 Python 2 vs Python 3 00:04:28 Duration

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

Lecture 1 How to Succeed in this Course (Long Version)
Lecture 2 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 00:22:04 Duration
Lecture 3 Machine Learning and AI Prerequisite Roadmap (pt 1) 00:11:19 Duration
Lecture 4 Machine Learning and AI Prerequisite Roadmap (pt 2) 00:16:07 Duration

Section 11 : Appendix FAQ Finale

Lecture 1 What is the Appendix 00:02:48 Duration