Course syllabus

Welcome to the course pages for EDAN95 - Applied Machine Learning!

General information

The course started on Monday, November 2nd, all places are filled according to the rules regarding prerequisites and ranking (see official course plan in Swedish or English).

Details about the course in HT2020 will be added here, for previous course runs you can have a look at the course home page. 

Covid-19 adaptation

Lectures will be held in Zoom, contact with teachers and TAs in exercise / lab sessions and office hours will be online  in a respective Discord server (lab sessions and office hours). The Discord server will also be available 24/7 for you to discuss with your peers. 

In addition, we will use this platform (Canvas) as the overall course platform to distribute information and course material - and of course it also offers opportunity for discussions with your peers.

We will also keep some of the originally scheduled lab hours in one computer room, so that those who do not have suitable equipment of their own can access machines with the necessary software installed.
NOTE that access is limited and will only be granted upon request.
NOTE also that the entry (if any) "E:Alfa" for the lab sessions refers to this offer only, there will be
no teachers or TAs available in the rooms for presentations or assistance!
Contact us in Discord (or through other channels as given on the contact page in case you have trouble with Discord) instead! 

Syllabus

Sessions and assignments: The course has 14 lectures, 7 programming assignments (labs) that should be worked on in pairs, and 4 individual homework assignments (reports).

Modules: The course has 15 Canvas modules, one for each lecture and related assignments, plus one for general information and preparation / recap material (an intro to Python (book chapter), a Python tutorial (Jupyter notebook), a "Lab 0" exercise, and a pre-recorded video "Lecture 1.5" with a recap on linear algebra).

Lectures will be held by three teachers, Elin A. Topp (EAT, course responsible), Pierre Nugues (PN), and Volker Krueger (VOK).

Teaching assistants: For the lab sessions and to support you in general with the course material, we have 6 teaching assistants; Alexander Duerr, Hampus Åström, Erik Gärtner, Dennis Medved, Leonard Papenmeier, and Matthias Mayr.

Contact information and office hours for teachers and TAs can be found here and in the course calendar.

Examination: If you are registered for course instances HT2019 and later, fulfilling all assignments (programming assignments in pairs, homework assignments individually) will give the full course credits (7.5 ECTS) and a passing grade "3", as well as allow you to take the optional exam, with which it is then possible to get a higher grade ("4", or "5").
If you are re-registered for course instance HT2018, fulfilling all programming assignments (in pairs) will give a part of the course credits (4.5 ECTS). To pass the entire course (7.5 ECTS) you need to also pass the exam. The grade achieved in the exam holds then for the entire course (grade scale "U/3,4,5,").
If you are a PhD student registered for the course, fulfilling all assignments (programming assignments in small groups including discussing them in a seminar session (upon agreement), homework assignments individually) will give the full course credits (7.5 ECTS) and a passing grade (G for "godkänd / passed") on the U/G scale. 

Overview over the course events per course / calendar week: