Statistical Machine Learning 1RT700 61808 VT2022

Course content

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. These methods will be studied and applied to real data from various applications throughout the course. Other important practical considerations that are covered include cross-validation, model selection, and the bias-variance trade-off. In addition to the necessary theory (e.g., derivations and proofs), this course also includes practical sessions (notably the lab and the mini-project). The practical part will be implemented using Python.

The book for this course is Machine Learning - A First Course for Engineers and Scientists. It is available online for free. Certain chapters will be recommended for 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!

Course structure

Click on the links above to get further information about each course element.

Course evaluation

The course evaluations (general, STS) and the course report are available for download.

Schedule

The schedule is available in TimeEdit.

Note that the first teaching session of the day starts at 8:00 a.m. instead of 8:15 a.m. The teaching session after lunch starts at 1 p.m. instead of 1:15 p.m. This is to give the opportunity for a 30 min break between physical and digital teaching sessions.

COVID plan

The course will be a hybrid campus and online teaching.

Lectures will be given on Zoom. Recordings will be made available on this home page.
Exercise sessions will be given on Zoom and on campus in parallel. Please observe that students are advised to wear face masks in the on-campus exercise sessions. The students themselves, not the institutions, have to provide the masks. If a teaching assistant has symptoms, the corresponding on-campus exercise session will be canceled and students referred to the Zoom alternative.
If the COVID-situation gets worse, all teaching will be given via Zoom at the same time slots as they should have been given on campus. If this happens, students will be informed via an Announcement.
Information about potential changes to the examination due to COVID will be announced on Feb. 15.

Zoom

We will use the same Zoom meeting room named "Statistical Machine Learning VT2022" for all teaching activities that are on Zoom. That includes the streamed lectures, the exercise sessions, and the labs. You can find them via "Zoom" in the left menu. It is required that you are logged in to Studium and registered to the course to see the meeting link. Also, the link might appear under "Previous meetings".

Exercise sessions

During the exercise sessions, the teaching assistant will give a short introduction of the material related to that session and during the session provide insight and comments on the solutions. Except for this, you are supposed to be active and work with the suggested exercises and the teaching assistants will be available for questions and discussion. Take this opportunity to interact! The exercises will be available ahead of time before the session. There are two time slots for each exercise session in the schedule. Choose the one that fits best with your schedule.

Grading

There will be a written final exam on Thursday, March 10. To pass the course you need to pass the mini-project, lab and exam. Grade VG on the mini-project will increase your grade one step, but not from U to 3.

grading

Teachers


Jens Sjölund (course responsible, lecturer)	Thomas Schön (lecturer)	Sebastian Mair (lecturer)	Niklas Wahlström (lecturer)

Jennifer Andersson (teaching assistant)	Daniel Gedon (teaching assistant)	Fredrik Gustafsson (teaching assistant)	Philipp Pilar (teaching assistant)

Paul Häusner (teaching assistant)	Dmitrijs Kass (teaching assistant)	Jingwei Hu (teaching assistant)