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

General

• The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

  • I think I got this from Sam Altman recommending it on Twitter. It looks pretty interesting.

Imperfect information game players

Libratus

• 2017.12.26 - YouTube - Noam Brown - Superhuman AI for heads-up no-limit poker: Libratus beats top professionals

  • TODO: Summarize this.

Reinforcement learning

Articles

• Quora - What distinguishes reinforcement learning from deep learning?

  • My favorite answer:

    • Deep learning is an approach to implementing function approximation.

      Reinforcement Learning is a learning problem in which the goal is to learn from interaction how to act in an environment to maximize a reward signal. There are many different algorithms to solve RL, many which involve function approximation, which when coupled with the above is what leads to deep reinforcement learning.

Websites

• Reinforcement Learning Warehouse - Tutorials

Books

• Amazon - Reinforcement Learning: An Introduction

  • Highly rec'd book

AlphaZero

• 2017.10.28 - Medium - David Foster - AlphaGo Zero Explained In One Diagram

• 2018.01.26 - Medium - David Foster - How to build your own AlphaZero AI using Python and Keras

Non-deep-learning classifiers

Naive Bayes

Related pages

• Bayes' Theorem

Tutorials

• 2017.05.25 - MonkeyLearn.com - A practical explanation of a Naive Bayes classifier

• 2017.09.11 - Analytics Vidhya - 6 Easy Steps to Learn Naive Bayes Algorithm (with codes in Python and R)

  • In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.

Gradient Descent

• http://ruder.io/optimizing-gradient-descent/

  • This is apparently a "plain English" explanation of different gradient descent algorithms. I found out about it from a tweet that was retweeted by Jeremy Howard.

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.

• 2017.10.24 - Rainforest QA - Kaggle Perks in Real Data Science Work

  • He rec's the following articles / forum posts:

    • For non-trivial validation:

      • Validation for pairs of items with a time component

      • Time-series validation when you need to predict for some specific time in the future

      • TODO: Grab and organize the rest of the links in this post.

    • https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/discussion/36809

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

• Kaggle - Learn

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