Welcome to the course Applied Machine Learning, 3 credits, given by Linnéuniversitetet. My name is Johan Hagelbäck and I am the main instructor and course manager on this course. You can reach me on email (firstname.lastname@example.org), Slack (sign up on coursepress.slack.com) or on phone 0480-497707.
About the course
The course mainly consists of self-studies where you watch pre-recorded lectures and read corresponding chapters in the course literature. We will have three physical meetings in the course: Introduction, Workshop 1 and Workshop 2. It is not mandatory, but highly recommended, to attend these meetings. At the workshops we discuss the subject in more detail and work with practical programming tasks, so you need to bring your laptop to them. The schedule for the meetings can be found at coursepress.lnu.se/kurs/applied-machine-learning/schedule/
The course is an advanced course in computer science, so it is assumed that you have some programming skills. If not, you can still pass the course but it will require substantially more work.
If you plan to take the course as a full distance course you don’t need to attend any of the physical meetings.
- The Introduction lecture is pre-recorded (see coursepress.lnu.se/kurs/applied-machine-learning/l00/).
- The practical tasks for Workshop 1 can be found at coursepress.lnu.se/kurs/applied-machine-learning/workshop-1/.
- The practical tasks for Workshop 2 can be found at coursepress.lnu.se/kurs/applied-machine-learning/workshop-2/.
Depending on your programming skills, it can be difficult to solve and understand the practical tasks on your own. If you need help, you can always contact me on Slack and we can schedule an online meeting.
If you are new to programming, I recommend reading the book Fundamentals of Python Programming available for free here. If you have programming experience but haven’t used Python before, I recommend reading the book A Whirlwind Tour of Python available for free here.
Where do I start?
All course material (pre-recorded lectures, practical tasks, reading recommendations, etc.) can be found at the course page at
coursepress.lnu.se/kurs/applied-machine-learning/. Start by watching the first four (and the Introduction lecture if you don’t attend it physically) and follow the preparations for Workshop 1. You shall also start thinking about your project work (see coursepress.lnu.se/kurs/applied-machine-learning/project/ for more details).
All submissions will be done in the teaching platform MyMoodle (mymoodle.lnu.se). You will automatically be added to the correct course room when you have been admitted to the course and have created a student account (see instructions below). There are two submissions in the course:
- Project plan
As a new student at Linnéuniversitetet you need to go through a few steps:
- Obtain a student account (go to lnu.se/student/ny-student) in order to be able to log in to lnu.se/student which will give you access to information, see your credits, get access to internet and much more (if you haven’t studied at Linnéuniversitetet before).
- Registration/confirmation that you will actually take the course that you have been accepted to.
After the course
When you have completed the course you will find your credits via your student account, here you can print out proof of taking the course if you need to provide your employer with this information. If you want a nicer course certificate you can apply for a certificate via email@example.com, the certificates will be signed by the degree evaluation officer at the Office of Student Affairs.
I hope you will enjoy the course and don’t hesitate to contact me if you have any questions!
Dr. Johan Hagelbäck