263-3210-00L  Deep Learning

SemesterAutumn Semester 2017
LecturersT. Hofmann
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 300.



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 conditions:
1) The number of participants is limited to 300 students (MSc and PhDs).
2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below:

Machine Learning
https://ml2.inf.ethz.ch/courses/ml/

Computational Intelligence Lab
http://da.inf.ethz.ch/teaching/2017/CIL/

Learning and Intelligent Systems
https://las.inf.ethz.ch/teaching/lis-s17

Statistical Learning Theory
http://ml2.inf.ethz.ch/courses/slt/

Computational Statistics
https://stat.ethz.ch/education/semesters/ss2012/CompStat/sk.pdf

Probabilistic Artificial Intelligence
https://las.inf.ethz.ch/teaching/pai-f16

Data Mining: Learning from Large Data Sets
https://las.inf.ethz.ch/teaching/dm-f16

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 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 examinationStudents are offered a project (40 hours) with bonus effect on the grade.
Grade = {exam, 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.

Courses

NumberTitleHoursLecturers
263-3210-00 VDeep Learning2 hrs
Mon13-15ETF C 1 »
T. Hofmann
263-3210-00 UDeep Learning1 hrs
Mon15-16CAB G 51 »
16-17ML F 36 »
T. Hofmann

Groups

No information on groups available.

Restrictions

Places300 at the most
Waiting listuntil 03.10.2017

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