Suggested work for this block

4 hours lectures, 2 hours paper and pencil, 2 hours computer lab, 2 hours of reading from course book, 2 hours paper and pencil self-study. 2 hour form mini-project group. Total 4 hours.

You need to form a group for the mini-project. This is an examined component of the course and it is important we get stated early.

Lecture F3: Classification and logistic regression

What's the difference between classification and regression?

An introductory video:

However, this video also illustrates (unknowingly it appears) one serious problem in current day machine learning: stereotyping and bias. Classifying gender based on hair length is not a valid or useful application of machine learning. We will come back to this point later in the course.

Lecture F3 on Thursday Nov 4th @ 13.15-15.00
Recommended reading: Ch. 3.2

Slides for the lecture

Lecture F4: Discriminant Analysis and k-Nearest Neighbour

Lecture F4 on Thursday Nov 8th @ 10.15
Recommended reading: Ch. 10.1 and 2.2

Slides for the lecture

Tutorial L3 (pen & paper)

Start this work on Nov 8th. Spend about two hours attempting the questions before the tutorial.

The exercise sheet is available here.

Recommended problems: 3.1, 3.2, 3.3, 3.5
Additional problems: 3.4, 3.6, 3.7

Tutorial L4 (computer)

Start this work on Nov 11th. Spend about two hours attempting the questions before the computer lab.

The computer labs are available here:

Python notebook
Google Colab

Recommended problems: 4.1, 4.2, 4.3, 4.4
Additional problems: 4.5

Suggested solutions are available here.

Want to learn more?

Geometry of logistic regression

This video gives a more in-depth mathematical/geometric interpretation of logistic regression. This is good up until the end where, I get confused what he is drawing.

Here he also uses males and females in the example, but this is not problematic as an application simply because men and women do have different health issues due to differences in biology.

Applications in football

David Sumpter made videos about fitting an expected goals model in football using logistic regression. These are from the course 'Mathematical Modelling of Football':