1. Overview

Machine Learning

Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).

Can also be defined as:

• “Computational methods using experience to improve performance or to make accurate predictions”.
• “A computer observes some data, builds a model based on the data, and uses the model as both a hypothesis about the world and a piece of software that can solve problems”.
• “A set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data”.

Link to original

Types of Machine Learning

• Supervised Learning

  Supervised Learning: the program is ‘trained’ on a given set of examples (with labels). It learns how to reach an accurate conclusion when given new data.

  Link to original

• Unsupervised Learning

  Unsupervised Learning: the program is given a bunch of unlabelled data and must discover patterns and relationships in them.

  In unsupervised learning:

  • we are provided with a data set
  • we need to find an understanding of interesting relationships within the data
  • we identify groups / clusters of interesting information within the data
  • can be used to identify where new points ‘fit’ within the data set

  We use two common techniques: clustering and association rules.

  Link to original

• Reinforcement Learning

  Reinforcement Learning: the program learns from consequences of its actions (reward or punishment), rather than from being explicitly taught and selects its actions on basis of its past experiences (exploitation) and also by new choices (exploration).

  Link to original

Classes of Learning Problems

• Classification Problem (AI)

  Classification (supervised): organise data in classes and determine the class of new data points $y\in \{1,\dots ,C\}$ Where $C$ is the number of classes.

  Common form is binary classification where $C=2$, often written as $\{0,1\}$. If $C>2$ then we have multiclass classification.

  Link to original

• Regression Problem (AI)

  Regression (supervised): fit functions to data and determine values of new data points Similar to classification, but the output is instead continuous.

  Link to original

• Clustering Problem (AI)

  Clustering (unsupervised): separate data in groups and determine the group of new data points

  Link to original

• Dimensionality Reduction Problem (AI)

  Dimensionality Reduction (unsupervised): transform high-dimensional data into lower-dimensional data while preserving desired properties

  Link to original

Training and Training Sets

• Training Set

  Training Set: portion of data used for learning (consists of labels and data)

  Link to original

• Test Set

  Test Set: untouched portion of data used to evaluate model after training

  Link to original

• Accuracy (AI)

  Accuracy: given a classification algorithm and a test set, the accuracy is the proportion of correct predictions done by the algorithm over all predictions in the test set $\text{accuracy}=\frac{\text{correct}}{\text{total}}$

  Link to original