# Week 2

# Classification, Regression, Error Functions and Optimization

### Lecture Notes

Classification, Regression, Error Functions and Optimization

### Learning Outcomes Week 2

In this lecture, the following concepts were introduced.

- An overview of the idea of classification. Including
- Understanding a basic classification algorithm like the
*perceptron algorithm* - Understanding what a
*feature*matrix is. - Understand what the
*data labels*are. - The concept of a
*learning rate* - The concept of linear separability

- Understanding a basic classification algorithm like the
- An overview of the idea of regression. Including
- Basis functions can be used to make a linear regression non-linear.
- An example of a commonly used basis set (like polynomials or radial basis functions).
- A commonly used error (or objective) function such as the sum of squared errors.

- The difference between a
*model*and an*algorithm* - The concept of
*generalization* - The idea of a
*training set* - The use of the
*error function*(also known as an*objective function*) - The
*importance*of the mathematical concepts of- vectors
- differentiation
- minimum

- The idea behind the optimization approach of steepest descent.
- How stochastic gradient descent differs from steepest descent and why this is important.