Table of contents

Child pages

Career advice

• 2017.03.01 - Fast.ai - How to change careers and become a data scientist - one quant's experience
• 2017.04.06 - Fast.ai - Alternatives to a Degree to Prove Yourself in Deep Learning

Challenge sites

• AI Challenge: Ants
• Starcraft
  • AIIDE Starcraft AI Competition
  • [SSCAIT] Student StarCraft AI Tournament

Reinforcement learning

Websites

• Reinforcement Learning Warehouse
  • Reinforcement Learning Warehouse - Tutorials

Books

• Amazon - Reinforcement Learning: An Introduction
  • Highly rec'd book

Non-deep-learning classifiers

Naive Bayes

• MonkeyLearn.com - A practical explanation of a Naive Bayes classifier

Major websites

• KDnuggets

Minor websites

• Otoro - Machine Learning
• Ferenc Huszár
• DataSchool.io - In-depth introduction to machine learning in 15 hours of expert videos

Organizations

• OpenAI

Companies

• Indico
• Microsoft: Project Oxford

Learning resources

Articles

• 2017.07.28 - Mashable - This high school kid taught himself to be an AI wizard

  • incredibly, Mikel's programming skills in machine learning and AI are almost completely self-taught — there are no courses on building AI-powered systems at his high school in Guildford, just outside of London. He spent big chunks of the last couple of years researching AI and machine learning on the internet, reading articles and watching videos.

    "There are lots of free courses online, but I actually didn't take courses. When I had a big problem I wanted to solve, I would just Google around and try to find out about it, so I didn't follow any predefined track. I read about Kaggle online, and I thought, 'Why not try it?'"
    
    After entering various competitions over several months and slowly placing higher and higher, Mikel helped create an algorithm for using computer vision to analyze 8 million YouTube videos to create accurate tags. His team ended up 7th out of 650 teams.

Bootcamps

• Zipfian Academy
  • This is the one Justin went to.

Topic-specific

Markov Chains

• 2015.10.05 - Nicole White - Improving My CLI’s Autocomplete with Markov Chains
  • This is very easy-to-follow; it would be easy to use the code here as a template for another project.

Computer Vision

• Tutorials
  • www.pyimagesearch.com
    • This guy seems really smart, I'd really like to work with him.
• Talks
  • 2014 - Stanford Engineering's Fei-Fei Li explores visual intelligence in computers

Books

• Erik Mueller (he was on the IBM Watson team)
  • Daydreaming in Humans and Machines: A Computer Model of the Stream of Thought
  • Commonsense Reasoning, Second Edition: An Event Calculus Based Approach
• The Master Algorithm
  • Apparently a good overview of all of the ML stuff out there.
• Programming Collective Intelligence
  • Rec'd by someone who commented on another person's review for 'The Master Algorithm'.

Courses

• CalTech - The Learning Problem

Papers

• A Model Explanation System
  • rec'd by Bob

Challenge sites

• Kaggle
• Numerai
  • AI prediction of the stock market using encrypted data. Interesting concept!

Libraries

• http://scikit-learn.org/
• http://scikit-image.org/
  
• simple-amt
  • This is a wrapper for Amazon Mechanical Turk, useful for labelling data

• 2015.11 - Google Research - Computer, respond to this email.

Genetic programming

Books

• A Field Guide to Genetic Programming
• Introduction to Evolutionary Computing
• Genetic Programming by John Koza