Machine Learning
The course addresses the question of how to enable computers to learn from previous experiences.
First, the machine learning field is introduced, describing a variety of learning paradigms, algorithms, theoretical results and applications. Then basic concepts from statistics, information theory and probability theory are introduced to the degree they are relevant to machine learning.
The course deals with advanced models such as deep learning and focuses on both theory and applications of these models. The course covers ethical aspects of machine learning models, model prejudices at different levels and how they can be handled in an effective way.
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Course structure
The language of instruction is English.
Teaching format
The teaching activities in the course are: lectures and exercises.
Assessment
The course is examined as follows:
- on-campus examination and
- assignments
Examiner
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course. -
Course literature
Note that the course literature can be changed up to two months before the start of the course.
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Contact
This course is part of a programme, course package or incoming exchange studies and is not available for application as a stand-alone course.
Find more information about Master’s programmes here
Student counsellor - master
- Visiting address
Nod Buildning, Borgarfjordsgatan 12, Kista
- Office hours
Please contact us via email if you want to book a meeting. We are available on Campus in Kista and via Zoom.
- Phone hours
Thursday 12.30–2 pm
