# ML Wiki

## Machine Learning

Machine Learning - field of study that gives computers the ability to learn without being explicitly programmed

A computer is said to learn from experience $E$ with respect to some task $T$ and some performance measure $P$

• if it's performance of $T$,
• as measured by $P$,
• improves with experience $E$

E.g. email filtering:

• $T$: classifying email as spam/not spam
• $E$: watching you label as spam/not spam
• $P$: the number of emails correctly classified as spam/not spam

Examples of Machine Learning:

• db-mining
• web-data clicks, etc
• automation
• autonomous helicopter
• NLP
• Computer Vision
• self-customizing software
• amazon, netflix, etc
• understanding human learning

## Supervised Learning

• i.e. the algoritm takes a training set $\{(x^{(i)}, y^{(i)})\}$
• and then predicts a value for / classifies a data example $x$

### Regression

Regression - predict: continuous values

Examples:

• You have a large inventory of identical items
• You want to predict how many of these items will sell over the next 3 months

Main tool

### Classification

Classification - assigning to a group (0, 1) etc: discrete values

$y \in \{0, 1\}$ - binary classification problem

• 0 - negative class, connected with absence of smth (not spam)
• 1 - positive class, connected with presence of smth (spam)

Tools:

## Unsupervised Learning

• just given the data, no labels
• can we find a structure in the data?
• we don't tell the algorithm what are the categories

i.e. we're given only $\{x^{(i)}\}$, no $y^{(i)}$s

applications

• news segregation
• social network analysis
• clustering
• market segregation

### Clustering

The goal is to automatically group the data into coherent subsets (or clusters)