# Dragon Notes

UNDER CONSTRUCTION
Latest content:
 Feb 20 Regression - Data Science Feb 19 +Classification - ML Feb 18 Control Design - Control Sys. Feb 16 Solved Problems - Data Sci. Feb 12 +Control Systems - MATLAB Feb 10 Image Recognition - Digits:ML Algorithms

# Data Science

Subtopics

Basics

Supervised vs. Unsupervised Learning

 Supervised Unsupervised - Correct output is known - Goal is to derive relationship between input and output: Regression: mapping inputs to a continuous output function Classification: mapping inputs to representative discrete outputs - Correct output is not known - Goal is to derive structure from data where the effects of variables aren't necessarily known - Works by clustering data based on relationships among variables in the data - No feedback based on prediction results Examples: Regression: given a picture of a person, predict their age on the basis of the given picture Classification: given a patient with a tumor, predict whether the tumor is malignant of benign Examples: Clustering: given a collection of 1,000,000 different genes, find a way to automatically group these genes so that they are somehow similar or related by different variables, such as lifespan, location, roles, etc. Non-clustering: given a mesh of sounds at a cocktail party, identify individual voices and music (finding structure in a chaotic environment)