This course teaches you to formulate real-life aspects, such as finding blueberries during a hike, as machine learning problems. You
will learn how to solve these machine learning problems using the most
basic and widely used machine learning methods in the programming
language Python.
Our
main ambition is to provide students with a minimum skill-set that
allows them to make good use of ready-made machine learning libraries
(such as this here). We do not focus on detailed derivations of calculations that are carried out within machine learning methods.
This course is for everybody with some experience in using a higher-level language such as Java, C++ or Scala. You should also be familiar with using vectors and matrices to arrange numbers that represent data points.
Grading. Students
can collect points by choosing freely from different tasks. These tasks
include six coding assignments each worth 100 points. Another task is a
student project (worth up to 600 points). You can verify the current
amount of points you have achieved during the course in the Mycourses
gradebook.
Student Projects.
We
will also offer mini projects where students have to formulate an ML
problem arising from some real-life applications. These applications
could arise from research, study, or every-day life aspects. The grading
of these projects will be based on a report in the form of a Python
notebook with a prescribed outline. These reports might serve as the
basis for a (BSc or MSc) thesis or even a publication.
At
the end of the course, you will be able to apply simple machine
learning methods for your own projects and will be prepared to learn
more complex concepts such as deep learning.
Slack Discussion Forum
Course Slack can be joined by using this link.