Machine Learning

Intro

Machine learning is a core subbranch of AI. In general, ML is about learning to do better in the future based on what was experienced in the past.

Learning Algorithms

As usual, we care about time and space efficiency. But we also care a great deal about the amount of data we need. 3 criteria for successful learning:

1. enough data
2. a rule that makes a low number of mistakes on the training data
3. make the rule as simple as possible Very often there is trade-off between 2 and 3.

Learning Models

• Example (or instance) is the object being classified
• An example is described by a set of attributes (aka features, variables, or dimensions)
• Label (or class) is the category
• Concept is the mapping from examples to labels. $c: X \rightarrow \{0, 1\}$
• Concept class is a collection of concepts

Ex. A patient might be described by gender, age, weight, blood pressure, body temp, etc.

During training, the learning algorithm is supplied with labeled examples. During testing, only unlabeled examples are provided.

We often assume only two labels, 0 and 1. We also assume there is a mapping from examples to labels. $c: X \rightarrow \{0, 1\}$. X is the space of all possible examples (aka domain or instance space).

Reference

COS511 Theoretical Machine Learning by Rob Schapire