Statistical Machine Learning 1RT700 11808 HT2020

This is an introductory course to statistical machine learning for students with some background in calculus, linear algebra and statistics. The course is focusing on supervised learning, i.e, classification and regression. The course will cover:

Week 1: Introduction, Recap of Probability Theory, Linear Regression.

Week 2: Logistic regression, k-NN and discriminant analysis.

Week 3: Bias-variance trade-off, cross validation.

Week 4: Tree-based methods, bagging and boosting.

Week 5: Feature selection and applications.

Week 6-7: Neural networks.

Week 8: Societal and ethical aspects of machine learning.

These methods will be studied and applied to real data from various applications throughout the course. The course also covers important practical considerations such as cross-validation, model selection and the bias-variance trade-off. The course includes theory (e.g., derivations and proofs) as well as practice (notably the lab and the mini project). The practical part will be implemented using Python.

For those wishing to sharpen your Python skills there will be an introductory lecture on Monday of week 2. See more details here.

Reading and video material

The  book for this course is The Supervised Machine Learning book. It is available online for free. Certain chapters will be recommended reading each week. 

We also link to videos and other material. There is a lot of very good video material on Youtube, which can help in your learning. Use it as much as you like!

What you need to do each week

Each week will consist of a mixture of lectures, video material, reading, paper and pen and computer classes.  You will get the most from the course if you attend the lectures live (although all videos will be made available afterward too) and attempt the pen-and-paper and computer classes before the tutorial sessions.

Answers to tutorial will be posted directly before the tutorial sessions.

The course schedule is available in TimeEdit

You are expected to work 10-15 hours a week on this course. A suggested time to invest in the course is suggested at the start of each week. If you do this work you will, with a high probability, pass the exam.


Go here for details of the mini-project.

Thanks for all your mini-project reports! Each of you who has submitted a report should have one other report to review. The peer-review is done individually. Read and follow the peer-review instructions for how to find your peer-review assignment and how to enter your review. Especially, pay attention to the last comment in the instructions:

Wait until the end to fill in the rubric with its comments and grades, and fill it in one single attempt.

The deadline for the peer-review is on Friday December 11.

If you have questions regarding the mini-project, write in the discussion forum.

To pass the course you need to pass the mini-project, lab and exam. If you pass mini-project and lab, your exam grade will also be your course grade. Grade VG on the mini-project will increase your course grade by one step, but not from U to 3.

Computer lab

Go here for details of the computer lab.

Each group has to sign up for one of the computer lab sessions. Follow the instructions here. The lab instructions are also available from that page. Remember that every group has to submit solutions to the preparatory exercises in this assignment on Studium until Wednesday December 9. You only have to enter your solutions in a text field, you can't upload any files.

If you have any questions regarding the lab, please post them in the discussion section.

Course summary:

Date Details Due