263-3210-00L  Deep Learning

SemesterAutumn Semester 2023
LecturersT. Hofmann, N. Perraudin
Periodicityyearly recurring course
Language of instructionEnglish


263-3210-00 VDeep Learning3 hrs
Wed13:15-14:00ML D 28 »
Thu14:15-16:00ML D 28 »
T. Hofmann, N. Perraudin
263-3210-00 UDeep Learning2 hrs
Mon16:15-18:00HG G 5 »
Wed16:15-18:00HG G 5 »
T. Hofmann, N. Perraudin
263-3210-00 ADeep Learning2 hrsT. Hofmann, N. Perraudin

Catalogue data

AbstractDeep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
ObjectiveIn recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology.
Prerequisites / NoticeThis is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit.

The participation in the course is subject to the following condition:
- Students must have taken the exam in Advanced Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below:

Advanced Machine Learning

Computational Intelligence Lab

Introduction to Machine Learning

Statistical Learning Theory

Computational Statistics

Probabilistic Artificial Intelligence

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits8 credits
ExaminersT. Hofmann, N. Perraudin
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationThere is a mandatory project (40 hours).
Grade = 0.7 exam + 0.3 project
Written aidslimited aids (4 x A4 pages of notes)
Online examinationThe examination may take place on the computer.
Distance examinationIt is not possible to take a distance examination.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Main linkInformation
Only public learning materials are listed.


No information on groups available.


Places320 at the most
Waiting listuntil 07.10.2023

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