Recorded Webinar on Content Optimization with Multi-Armed Bandits & Python
An overview of visualization in Python
Overview
Whenever you have multiple items to choose from, and are not sure which will result in the highest level of engagement or action, you have to make a choice. Multi-armed bandit, a branch of machine learning, is the fastest, most efficient method to make such a choice. This course examines a learn-as-you-go online learning method called reinforcement learning. Typical applications of multi-armed bandits include subject line testing for emails, button colors, page design/layout, and headline optimization. Anything you can test in the A/B fashion, you can do with bandits.
*This is a recorded webinar.
- Intermediate
- 1 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
This webinar examines reinforcement learning, a learn-as-you-go online learning method. You will learn different strategies for balancing exploration and exploitation in order to learn the best action to take when you initially know nothing about the payoffs of the different actions. You will learn how to implement bandit algorithms, tune them, and incorporate them into various apps.
- Visualization in Python
- Multi-Armed Bandits: a way to maximize reward given uncertain payoffs
- Bandit algorithms: greedy, epsilon greedy, epsilon decreasing, exponential, upper confidence bound, and Bayesian
- Data Types: Static, Restless, and Volatile data
- Simulate bandit systems and visualize the results
- Application 1: Command line application that uses bandits
- Application 2: Website that uses bandits
Who Should Take This Course
Those with an interest in an overview of visualization in Python.
Our Instructors
Mr. Kris Wright
Mr. Kris Wright is an experienced data scientist who has worked in academia and industry. He is a PhD candidate at Old Dominion University in the Department of Modeling, Simulation, and Visualization Engineering. His dissertation focuses on the social network analysis and machine learning (predicting things on graphs). He is also a full-time data scientist as Cognitiv, a deep learning company located in Bethesda, MD, where he works on computational advertising and image recognition.
Course Syllabus
Week 0
- Visualization: Overview of visualization in Python.
- Multi-Armed Bandits: Bandits are a way to maximize reward given uncertain payoffs.
- Bandit algorithms we will cover: greedy, epsilon greedy, epsilon decreasing, exponential, upper confidence bound, and Bayesian
- Data Types: Static, Restless, and Volatile data will be covered.
- Static rewards exist forever and their expected payoff never changes
- Restless rewards exist forever and their expected payoff changes over time
- Volatile rewards exist for a certain period of time, then become unavailable
- Simulation: Simulate bandit systems and visualize the results.
- Application 1: Command line application that uses bandits.
- Application 2: Website that uses bandits.Â
Week 1
Students should be familiar with any high-level programming language (C++, Java, Python).
Prerequisites
Students should be familiar with any high-level programming language (C++, Java, Python).
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Recorded Webinar on Content Optimization with Multi-Armed Bandits & Python
Additional Information
Organization of Course
Recorded webinar: content is delivered via video that you may view at your leisure. There is no homework and no instructor interaction.
Time Requirements
Approximately 3 hours
Course Text
No required text. We have this suggested resource: When you launch Anaconda for Python, the Launcher program has many sample iPython notebooks on the right side. These are great tutorials for data analysis and visualization in python.
Software
You should have installed the Python 2.7 version of Anaconda, by Continuum Analytics. Useful links are below:
- Installing Python: https://wiki.python.org/moin/BeginnersGuide/Download
- Install virtualenv and virtualenvwrapper: http://docs.python-guide.org/en/latest/dev/virtualenvs/
- Get a Github account: https://github.com/
- Python Hello World: http://www.learnpython.org/en/Hello,_World!
- Using the terminal:https://learncodethehardway.org/
- Python programming: https://learnpythonthehardway.org/
- Anaconda: https://www.anaconda.com/products/individual
Course Fee & Information
Unlike standard Statistics.com courses, this recorded webinar is available on-demand and is not tied to a date.
Options for Credit and Recognition
There are no options for credit and recognition for this course.
Register For This Course
Recorded Webinar on Content Optimization with Multi-Armed Bandits & Python