We start with a short introduction of the course topics. Get to know each other and the other participants and explain the course structure and all the required tasks for a successful participation.

This requires i.e.: Give a project presentation and provide a well documented code with data via GitHub. Addtionally you are only allowed to miss two class sessions during the semester.

Give a presentation about your current status.....ideas in which direction you want to go......ask for feedback......be active

OPTIONAL:

Work on your semester project topic and get started.

This means:

Connect to your team members

Launch a GitHub Repository

Prepare your data

Last but not least - we would like to mention some very good book references. It is not required or requested to buy any of these books. If you are enrolled as a student at CAU, FH Kiel or another university you might have access to these books via the SpringerLink of your home institution. They cover the course material quite well and provide GitHub-Repositories for the codes which where used throughout the book. Unfortunately there is no 1:1-Coursera course matching these books perfectly - but we will do our best ;-)

Some additional course material and MOOCs if you want you expand your knowledge at your own pace:

Before the first class you should ...

• register with Google and the Coursera platform to get corresponding accounts. Since we switched for the second run our video lecture sources (the Coursera part is optional - but still benefical for the interested) - please see the YouTube-Playlists from below.

Please enroll/attend/complete the following Coursera MOOCs during the semester:

For the winter-term 2023/24 we use YouTube playlists.

Check out the more detailed playlists in each week.

Think about your project and get started....

Try to answer/motivate these questions:

What is you idea/topic?

Which data do you use?

How is it structured?

Is there something special you want to forecast?

....

The conditions to be met in order to receive a Certificate of Achievement (and ECTS) are:

• Attendance to at least 80% of the classes (it is allowed to miss maximum 2 times)

• Delivery of a ML-Time Series project and including the needed documentation (GitHub-repository is recommended)

• Upload of your presentation (PDF-file is preferred) to the Edu-Campus-System

Attendance:

If you attend via Zoom, please make sure to use your full name, which should be the same that you used to register at edu.opencampus.sh. Otherwise your attendance will not be recorded!

Please switch on your camera - ask questions - be (inter)-active!!

Projects:

Check the Projects section to learn more about the projects.

Continue working on your semester project !!!

For the semester project - you can bring your own data - we will discuss this in the first/second session. Nevertheless here you can find some resources to think about a potential project:

Check out these frameworks:

We will make a list - who goes first,second etc.

There will be a hard time limit.

Something about 15-30 minutes depending on the number of the final project presentations.

Finalize your semester project !!!

Finalize your semester project !!!

Check-Out these links:

http://adamian.github.io/talks/Damianou_GP_tutorial.html

https://jovian.com/nkafr/deepvar

Continue working on your semester project !!!

Define an appropiate approach for your problem

Formulate some questions about things you do not know

Finalize your semester project !!!

Work on your semester project !!!

Plot your data... ;-)

Think about an appropiate approach for your problem

Formulate some questions about things you do not know

Finalize your semester project !!!

Finalize your semester project !!!

Finalize your semester project !!!