Mailund on the Internet

In less than two hours, I give my last Machine Learning lecture before I’m heading off to the UK (I’m giving a few more when I get back). The slides from the lectures so far can be seen below:

Course Introduction

This is just a brief overview of what’s to follow. Motivates the topic and such.

Introduction to probability and statistics

This is two lectures covering the basic probability theory and statistics needed for the course. This is probably the most theoretical part of the course, and in my experience the material that confuses the students the most.

Linear Regression

This is the basic linear regression that you learn in just about any statistics class, except that there’s also a bit of Bayesian statistics and some model-selection/over-fitting theory that I haven’t seen in pure statistics classes (the classes I have taken have focused more on hypothesis testing approaches).

To get our students activated at this point, we also give them a simple project where they need to fit data to a linear model and try to predict target values based on their model.

Linear Classification

This, then, is today’s lecture. Classification, where the training consists of changing weights that split the predictor space into linear regions.

Slideshare

There seems to be some problems with some of the slideshare plugins above (some of the text is missing from time to time), but if you are interested you can find the slides (in PDF or OpenOffice format) here and you can see the course description and schedule here.