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This course is all about A/B testing.
A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more.
A/B testing is all about comparing things.
If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B”, well you can’t just say that without proving it using numbers and statistics.
Traditional A/B testing has been around for a long time, and it’s full of approximations and confusing definitions.
In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.
First, we’ll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.
You’ll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.
We’ll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.
Finally, we’ll improve on both of those by using a fully Bayesian approach.
Why is the Bayesian method interesting to us in machine learning?
It’s an entirely different way of thinking about probability.
It’s a paradigm shift.
You’ll probably need to come back to this course several times before it fully sinks in.
It’s also powerful, and many machine learning experts often make statements about how they “subscribe to the Bayesian school of thought”.
In sum - it’s going to give us a lot of powerful new tools that we can use in machine learning.
The things you’ll learn in this course are not only applicable to A/B testing, but rather, we’re using A/B testing as a concrete example of how Bayesian techniques can be applied.
You’ll learn these fundamental tools of the Bayesian method - through the example of A/B testing - and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future.
See you in class!
All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples
In the directory: ab_testing
Make sure you always "git pull" so you have the latest version!
HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:
TIPS (for getting through the course):
USEFUL COURSE ORDERING:
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|Section 1: Introduction and Outline|
What's this course all about?Preview
Where to get the code for this course
How to succeed in this coursePreview
|Section 2: Bayes Rule and Probability Review|
Bayes Rule Review
Simple Probability Problem
The Monty Hall ProblemPreview
Maximum Likelihood - Mean of a Gaussian
Maximum Likelihood - Click-Through Rate
What is the Bayesian Paradigm?
|Section 3: Traditional A/B Testing|
A/B Testing Problem Setup
Simple A/B Testing Recipe
Test Characteristics, Assumptions, and Modifications
t-test in Code
0.01 vs 0.011 - Why should we care?
A/B Test for Click-Through Rates (Chi-Square Test)
CTR A/B Test in Code
A/B/C/D/… Testing - The Bonferroni Correction
A/B Testing Pitfalls
Traditional A/B Testing Summary
|Section 4: Bayesian A/B Testing|
Explore vs. Exploit
The Epsilon-Greedy Solution
Bayesian A/B Testing
Bayesian A/B Testing in Code
The Online Nature of Bayesian A/B Testing
Finding a Threshold Without P-Values
Thompson Sampling Convergence Demo
Confidence Interval Approximation vs. Beta Posterior
|Section 5: Practice Makes Perfect|
Exercise: Compare different strategies
Exercise: Die Roll
Exercise: Multivariate Gaussian Likelihood
|Section 6: Appendix|
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Where to get Udemy coupons and FREE deep learning materialPreview
I am a data scientist, big data engineer, and full stack software engineer.
For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers.
I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.