Machine Learning & Half Moon Project

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This was our first Machine Learning assignment. Machine Learning is something that I was (and still am) super excited about. Basically, the algorithm 'learns' something, and can then apply it in other situations.

Learning & Neurons

This was the toughest concept to learn (ha). We started with a Neuron, similar to the neurons in our brains. The neuron can trigger (yes/no) based on a series of inputs, in our case two inputs. The equation for whether or not the neuron triggers is SUMMATION(Weight(n) * Input(n)) > threshold. Each input is weighted differently, and the weights are what we can change to 'train' the neuron. We use this equation: ΔWeight = learningSpeed * (target - output) * input. For example, if we start with a speed of 0.2, an input of 1, a weight of 0.5, and a target of 0, the weight would change by -0.2 (0.2 * (1 - 0) * 1). Now, with a weight of 0.3, repeat the process. Once each input returns the desired output, the best weights have been found.

Application: Half Moon Project

The Half Moon Project is a great application for the machine learning algorithm we learned. The goal is to separate two half-moons by a linear line.
In the example to the right, these half-moons cannot possibly be separated by a linear line, due to the fact that the distance between them is negative. However, if the distance is positive, the moons can be separated, as seen in the second picture.


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