Unsupervised Learning
Unsupervised learning is a type of machine learning, where instead of inferring a function from training data that can be used to map new examples, the task is to look for patterns in a data set without pre-existing labels.
The aim of the course is to introduce basic as well as modern concepts of statistical learning without training data (unsupervised statistical learning), with applications in statistical data analysis. Central concepts covered include similarity measures, linear and nonlinear methods of dimensional reduction, centroid, distributional and density-based methods of cluster analysis, hierarchical methods and different validation methods.
The course replaces the previous course with the same name and course code MT7039, and so cannot be included in the same degree as MT7039.
Information for admitted students autumn 2024
Congratulations! You have been admitted at Stockholm University and we hope that you will enjoy your studies with us.
In order to ensure that your studies begin as smoothly as possible we have compiled a short checklist for the beginning of the semester.
Follow the instructions on whether you have to reply to your offer or not.
universityadmissions.se
Checklist for admitted students
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Activate your university account
The first step in being able to register and gain access to all the university's IT services.
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Register at your department
Registration can be done in different ways. Read the instructions from your department below.
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Read all the information on this page
Here you will find what you need to know before your course or programme starts.
IMPORTANT
Your seat may be withdrawn if you do not register according to the instructions provided by your department.
Information from your department
On this page you will shortly find information on registration, learning platform, etc.
Welcome activities
Stockholm University organises a series of welcome activities that stretch over a few weeks at the beginning of each semester. The programme is voluntary (attendance is optional) and includes Arrival Service at the airport and an Orientation Day, see more details about these events below.
Your department may also organise activities for welcoming international students. More information will be provided by your specific department.
Find your way on campus
Stockholm University's main campus is in the Frescati area, north of the city centre. While most of our departments and offices are located here, there are also campus areas in other parts of the city.
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For new international students
The aim of the course is to introduce basic as well as modern concepts of statistical learning without training data (unsupervised statistical learning), with applications in statistical data analysis. Central concepts covered include similarity measures, linear and nonlinear methods of dimensional reduction, centroid, distributional and density-based methods of cluster analysis, hierarchical methods and different validation methods.
The course replaces the previous course with the same name and course code MT7039, and so cannot be included in the same degree as MT7039.
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Course structure
The course consists of two modules, theory and hand-in assignments.
Teaching format
Instruction is given in the form of lectures, exercise sessions and supervision.
Assessment
Assessment takes place through a written exam, and home exam of the hand-in assignments.
Examiner
A list of examiners can be found on
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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.
Hastie, Tibshirani & Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed). Springer.
Bishop: Pattern Recognition and Machine Learning. Springer.
Lee & Verleysen: Nonlinear Dimensionality Reduction. Springer.
Articles.
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More information
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Contact
