Machine Learning

Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed." - https://en.wikipedia.org/wiki/Machine_learning

Terminology and concepts

• Supervised machine learning: The program is "trained" on a pre-defined set of "training examples", which then facilitate its ability to reach an accurate conclusion when given new data.
• Unsupervised machine learning: The program is given a bunch of data and must find patterns and relationships therein.
• "The goal of ML is never to make 'perfect' guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful."
• Machine learning builds heavily on statistics.

Prerequisites

• Statistics
• Linear Algebra
• Calculus

Resources

• Reddit /r/machinelearning wiki
• Data Science From Scratch book
• Andrew Ng's Coursera course on ML
• Machine Learning with Python / Practical Machine Learning Tutorial with Python Introduction
• Your First Machine Learning Project in Python Step-By-Step
• Example Machine Learning IPython Notebook
• FastML: Machine Learning Made Easy
• Tensorflow
• My Neural Network isn't working! What should I do?
• Machine Learning Recipes with Josh Gordon - Google Developers

See Also

• Life 3.0: Being Human in the Age of Artificial Intelligence: https://www.amazon.com/Life-3-0-Being-Artificial-Intelligence/dp/1101946598
• DeepMind and Blizzard open StarCraft II as an AI research environment
• Intuitive RL: Intro to Advantage-Actor-Critic (A2C)
• Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.
• Creating music through image generation of spectrograms.