Linear Regression (continued)

Linear Regression (continued)

Professor Ameet Talwalkar

Professor Ameet Talwalkar

CS260 Machine Learning Algorithms

February 6, 2017 1 / 39

Outline

1 Administration 2 Review of last lecture 3 Linear regression 4 Nonlinear basis functions

Professor Ameet Talwalkar

CS260 Machine Learning Algorithms

February 6, 2017 2 / 39

Announcements

HW2 will be returned in section on Friday HW3 due in class next Monday Midterm is next Wednesday (will review in more detail next class)

Professor Ameet Talwalkar

CS260 Machine Learning Algorithms

February 6, 2017 3 / 39

Outline

1 Administration 2 Review of last lecture

Perceptron Linear regression introduction 3 Linear regression 4 Nonlinear basis functions

Professor Ameet Talwalkar

CS260 Machine Learning Algorithms

February 6, 2017 4 / 39

Perceptron Main idea

Consider a linear model for binary classification wTx

We use this model to distinguish between two classes {-1, +1}.

One goal

= I[yn = sign(wTxn)]

n

i.e., to minimize errors on the training dataset.

Professor Ameet Talwalkar

CS260 Machine Learning Algorithms

February 6, 2017 5 / 39

 ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download