Machine Learning: Supervision Optional

Ernest Tavares III
Oct 29, 2016 · 3 min read

Machine learning is defined as a subfield of computer science and artificial intelligence which “gives computers the ability to learn without being explicitly programmed” (source).

Although the statistical techniques which underpin machine learning have existed for decades recent developments in technology such as the availability/affordability of cloud computing and the ability to store and manipulate big data have accelerated its adoption. This essay is meant to explore the most popular methods currently being employed by data scientists such as supervised and unsupervised methods to people with little to no understanding of the field.

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An example of a support vector machine (SVM) algorithm being used to create a decision boundary (via wikipedia)

Supervised

Example: Imagine a doctor trying to predict whether someone has HIV. He has the test results (outputs) and medical records (variables) for patients who have tested positive and negative for the disease. His task is to look at the records and develop a decisioning system so that when a new patient arrives, given just their medical record (variables) he can accurately predict whether or not they are HIV positive.

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A graph of a logistic regression

Unsupervised

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An example of K-Neighbors clustering via sourceforge

Semi-Supervised

Reinforcement learning

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via SlideShare

Reinforcement learning describes a situation in which humans provide the following:

  1. An environment (A Go board)
  2. A set of rules (The rules of Go)
  3. A Set of actions (All the actions that can be taken)
  4. A set of outcomes (Win or lose)

Then given the environment, rules and actions a computer can repeatedly simulate many, many games of Go to arrive at outcomes.

What makes reinforcement learning extremely powerful is the sheer number of times a computer can simulate unique games. By the time Alpha Go faced Lee Sedol, the top player in the world, it had simulated more games than Sedol could’ve ever hoped to play in his lifetime. Humans need to eat, sleep and take breaks, computers don’t they just require electricity.

There you have it, a quick and dirty overview of some of the more popular machine learning methods currently being employed.

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