## Conceptual and mathematical summary for machine learning

Machine learning makes use of multiple mathematical formulas and relations to implement the different tasks it can handle. Gathered in the following “cheat sheets” by Afshine and Shervine Amidi, the concepts for supervised and unsupervised learning, deep learning together with machine learning tips and tricks, probabilities, statistics algebra and calculus reminders, are all presented in details with the underlying math.

Based on the Stanford course on Machine Learning (CS 229), the cheat sheets summarize the important concepts of each branch with simple explanations and diagrams, such as the following table cover underfitting and overfitting.

 Underfitting Just right Overfitting Symptoms • High training error • Training error close to test error • High bias • Training error slightly lower than test error • Very low training error • Training error much lower than test error • High variance Regression illustration Classification illustration Deep learning illustration Possible remedies • Complexify model • Add more features • Train longer • Perform regularization • Get more data

The main machine learning cheat sheets can be found here:

Other mathematics and coding cheat sheets can be found here:

The complete cheat sheets can also be found on Github.

## What is Artifical Intelligence?

Algorithms, enabled by constraints, exposed by representations, that support the building of models targeted at thinking, perception and action, and the loops that tie them together.

Artificial Intelligence is applied through problem solving procedures, methods, techniques and algorithms.

## How to approach a problem

Generate solutions and test to obtain positive or negative results.

This approach involved building generators with certain properties: not redundant (should not give the same solution twice), they should also be informable (able to select a category and disregard other)

## Rumpelstiltskin principle

Being able to name what you’re talking about gives you power over it, to understand and solve problems. Naming things grants power over concepts.

## Difference between trivial and simple

Trivial ideas implies that they are worthless, useless. In AI, the most simple ideas are often the most powerful.

## The benefits of language

1. Enables to tell stories
2. Enables to marshal the resources of the perceptual apparatus. It lets us imagine things that we never saw before.