Linear Algebra and Learning from Data
The course can be considered as a complement of the linear algebra courses you have studied at our department, but at a more advanced level.
We'll pay more attention on how to abstract relevant mathematics and structures from applications for example data and how to apply the theory you've studied. The course will start with some elementary elements in linear algebra e.g. SVD, principal components, matrix norms, generalized eigenvalues and interlacing eigenvalues. In particular we'll deal with these topics in a numerical sounding way. Later we'll turn to an interactive treatment of linear algebra and subjects from data.
Course contents
- Basic computationally efficient algorithms for large matrices
- Principal Component Analysis
- Sparse and underdetermined systems and their relation to data compression
- Construction of neural networks and models for deep learning
- Fitting hyperparameters
- Selected topics on particular types of matrices
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Your seat may be withdrawn if you do not register according to the instructions provided by your department.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.
Read more
For new international students
We'll pay more attention on how to abstract relevant mathematics and structures from applications for example data and how to apply the theory you've studied. The course will start with some elementary elements in linear algebra e.g. SVD, principal components, matrix norms, generalized eigenvalues and interlacing eigenvalues. In particular we'll deal with these topics in a numerical sounding way. Later we'll turn to an interactive treatment of linear algebra and subjects from data.
Course contents
- Basic computationally efficient algorithms for large matrices
- Principal Component Analysis
- Sparse and underdetermined systems and their relation to data compression
- Construction of neural networks and models for deep learning
- Fitting hyperparameters
- Selected topics on particular types of matrices
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Course structure
The course consists of one element.
Teaching format
Instruction consists of lectures, exercises, and computer projects.
Assessment
The course is assessed through written examination and hand-in exercises.
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.
G. Strang: Linear Algebra and Learning from Data. Wellesley-Cambridge Press.
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Course reports
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More information
New student
During your studiesCourse web
We do not use Athena, you can find our course webpages on kurser.math.su.se.
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Contact
