JoeKurokawa

Learning ML Part I: The different kinds of Machine Learning.

Welcome to the first series of topics on machine learning. Each week I will post what I learned in Machine learning. I have no prior knowledge in this area and will attempt to learn things as I go using online tools. The core of my learning will be based on Georgia Tech's ML class which can be found on Udacity. https://www.udacity.com/course/machine-learning--ud262 , It is the same class that students take to earn credit in their online masters program. This week, I touch on the different types of machine learning. There are three types of machine learning algorithms:

1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning

Supervised Learning

Supervised learning is the task of creating a model that maps an input to an output based on example input-output pairs. The model has to infer what comes next according to the training data. For example we have ordered pair: (1,1)(2,4)(3,9) . The next logical conclusion should be (4,16) . The logical conclusion is that these points map to y= X ².  Given a set of training data we can use supervised learning to find the relation in a testing set.

Unsupervised Learning

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. There is an input but not specific mapping. Say for example you have an amplitude graph of recorded  narration. Without knowing what the recording is saying and just by analyzing the amplitude and wavelength we can use Unsupervised learning to predict what words are being said. Another example would be image processing. We understand that a certain arrangement of pixels is a cow and if we were given a similar arrangements of pixels we can use unsupervised learning to determine if the image is a cow or not.

Reinforcement Learning

The third type of machine learning is reinforcement learning.  It deals with how software agents ought to take actions in an environment as to maximize some notion of cumulative reward. For example. Let's say we have  a robot that can buy and sell stocks. Without knowing any of the rules of the stock market such as what is a good trading strategy vs. what is not, the algorithm will trade stocks based on given negative or positive feedback. It is rewarded when it makes money and not rewarded when it loses money. The way the robot goes about doing it is left up to itself without explicit code telling what to do. This is a technique used by OpenAI to create a AI that was able to beat the best players in the popular online game DOTA within two weeks of learning.

https://newatlas.com/open-ai-dota2-machine-learning/50882/