Deep Learning in Data Science
Data science is an interdisciplinary field, using skills and methods from computer science, mathematics and statistics to extract knowledge from (large) data sets.
Information for admitted students Spring 2021
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 wether 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Your seat may be withdrawn if you do not register according to the instructions provided by your department.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
You will learn to
- explain the basic the ideas behind learning, representation, and recognition of raw data,
- account for the theoretical background for the methods for deep learning that are most common in practical contexts,
- identify the practical applications in different fields of data science where methods for deep learning can be efficient (with special focus on computer vision and language technology).
Only students from the following programmes can apply: Master's Programme in Mathematical Statistics, Master's Programme in Actuarial Mathematics, and Bachelor's Programme in Computer Science.
This course is given jointly with KTH, and you can find more information about the schedule, course literature etc. on KTH's pages - see links below.
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Course structure
The course consists of two elements; practical exercises and theory.
Teaching format
The education consists of lectures.
Assessment
The course is assessed through a take-home exam, and written presentation of the practical exercises. For information on how to register for exams at KTH, see:
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. -
More information
New student
During your studiesCourse web
Registered students get access to the KTH course web in Canvas.
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Contact
