# Introduction ## Well Posed Learning Problems __Definition__: A computer program is said to __learn__ from experience _E_ with respect to some class of tasks _T_ and performance measure _P_ if its performance at tasks in _T_, as measured by _P_, improves with experience _E_. An example of a task, inspired by [MarI/O](https://www.youtube.com/watch?v=qv6UVOQ0F44): - Task _T_: Playing the first level of Super Mario Bros. - Performance measure _P_: How far the Mario character is able to advance. - Training experience _E_: Playing the level over and over.

An inductive bias is the set of assumptions needed to take some new input data, which the system has _not_ encountered before, and predict the output. E.g: a self-driving car may have been trained to avoid cats in the road, but suddenly it encounters a dog. What should it do? If the system does not have an inductive bias, then it has not learned anything other than reacting to the specific examples that it has been trained on. In that case, it is basically just a [key-value store](https://en.wikipedia.org/wiki/Key-value_database) for its training examples. An inductive bias is essential to machine learning.

## Decision tree learning with ID3 ## Overfitting ## Cross validation