263-3210-00L  Deep Learning

SemesterAutumn Semester 2019
LecturersT. Hofmann
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
263-3210-00 VDeep Learning
Vorlesung im HG F7 mit Videoübertragung im HG F5.
2 hrs
Thu15:15-17:00HG F 5 »
15:15-17:00HG F 7 »
T. Hofmann
263-3210-00 UDeep Learning1 hrs
Mon15:15-16:00CAB G 11 »
Thu17:15-18:00CAB G 11 »
T. Hofmann
263-3210-00 ADeep Learning1 hrsT. Hofmann

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
Link

Computational Intelligence Lab
Link

Introduction to Machine Learning
Link

Statistical Learning Theory
Link

Computational Statistics
Link

Probabilistic Artificial Intelligence
Link

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits5 credits
ExaminersT. Hofmann
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)
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.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

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