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.
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.
Overlapping courses
The material in this course is also covered in the course Statistical Deep Learning (MT7042), so these two should not be included in the same degree. Since we now have a course of our own covering this material, DA7064 Deep Learning in Data Science will be given for the last time in the spring 2022.
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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.
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More information
New student
During your studiesCourse web
Registered students get access to the KTH course web in Canvas.
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Contact