Statistical Deep Learning
The course treats basic as well as modern concepts of statistical learning in terms of artificial neural networks (deep learning), with applications in statistical data analysis.
Topics treated include feedforward networks, regularization and optimization of networks with many layers, convolutional networks, recurrent networks and validation methods. In addition, mathematical interpretations of networks are given, such as nonlinear regression with different link functions for the outcome variable. The course includes some of the following topics; autoencoders, representation learning, deep generative methods, and information theoretic concepts of deep learning.
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Course structure
The course consists of two parts: theory and hand-in assignments.
Teaching format
Instruction is given in the form of lectures, exercise sessions and supervision.
Assessment
The course is assessed through a written exam and home 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.
Goodfellow, Bengio, Courville: Deep Learning. MIT 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
