Suggested work for this block

6 hours lectures (including Python lecture), 1 hour video recommendations, 2 hours pencil and paper self-study, 2 hour computer lab self-study, 2 hours paper and pencil tutorial. Total 13 hours.

Lecture F1: Introduction to supervised machine learning

What is supervised machine learning?

Here are two introductory videos to the topic of supervised machine learning.

Lecture F1 on Friday Oct 29th @ 15.15-17.00

Recommended reading: Ch. 1 and 2.1

Slides for the lecture

Lecture F2: Machine learning approach to linear regression

What is linear (in-the-parameters) regression?

The following introductory videos provide some fundamental concepts. The first video considers a single covariate (input) variable x

The next video extends the prediction problem to multiple covariates (input) variables x

Lecture F2 on Tue Nov 2nd @ 13.15-15.00

Recommended reading: Sec. 3.1 and 3.3.

Slides for the lecture.

Tutorial L1 (pen & paper): Linear regression

Start this work on Nov 2nd. Spend about two hours attempting the questions before the tutorial.

The exercise sheet is available here.

Recommended problems: 1.1, 1.2, 1.3
Additional problems: 1.4, 1.5

This will be covered at the tutorial.

Please attempt the questions before the tutorial. Also feel free to ask questions in the discussion section.

Tutorial L2 (computer): Linear regression

Don't forget the python intro lecture if you haven't watched it already!

Start this work on Nov 4th. Spend about two hours attempting the questions before the tutorial session. In particular, make sure you have a Python environment set up on your computer.

All the computer labs will be in the form of Python notebooks, You can use these either locally on you laptop (I recommend downloading Anaconda) or using Google Colab notebook.

The first lab sessions are available here:

Recommended problems: 2.1, 2.2, 2.3, 2.4
Additional problems: 2.5

Suggested solutions are available here.

Please attempt the questions before the tutorial. Also feel free to ask questions in the discussion section.

Want to learn more?

There are countless books and articles that introduce the topic of linear regression. To fully grasp it requires mastering the fundamentals of linear algebra. One resource is the comprehensive lecture notes by Brandon Stewart. The following slide provides some statistical intuition:

The World Happiness Report primarily uses linear regression to determine what makes us happy throughout the world. Below are the results of a regression over many different countries.

The biggest limitation of this kind of study is that it does not deal with causation. The machine only learns associations in the data. It is very important as an aspiring 'machine learner' that you understand how your statistical work ties in to scientific experimental and observational work. A very good example of how this is done can be found in the work of Lara Aknin and co-workers, also in the World Happiness Report.

If you want to investigate the data for yourself, it is available here.