263-3210-00L Deep Learning
| Semester | Autumn Semester 2024 |
| Lecturers | T. Hofmann |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 263-3210-00 V | Deep Learning | 3 hrs |
| T. Hofmann | ||||||
| 263-3210-00 U | Deep Learning | 2 hrs |
| T. Hofmann | ||||||
| 263-3210-00 A | Deep Learning | 2 hrs | T. Hofmann |
Catalogue data
| Abstract | Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations. |
| Learning objective | In 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 / Notice | This 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 https://ml2.inf.ethz.ch/courses/aml/ Computational Intelligence Lab http://da.inf.ethz.ch/teaching/2019/CIL/ Introduction to Machine Learning https://las.inf.ethz.ch/teaching/introml-S19 Statistical Learning Theory http://ml2.inf.ethz.ch/courses/slt/ Computational Statistics https://stat.ethz.ch/lectures/ss19/comp-stats.php Probabilistic Artificial Intelligence https://las.inf.ethz.ch/teaching/pai-f18 |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 8 credits |
| Examiners | T. Hofmann |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling. |
| Mode of examination | written 120 minutes |
| Additional information on mode of examination | There is a mandatory project (40 hours). Grade = 0.7 exam + 0.3 project |
| Written aids | limited aids (4 x A4 pages of notes) |
| Digital exam | The exam takes place on devices provided by ETH Zurich. |
| Distance examination | It 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 link | Information |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| Places | 320 at the most |
| Waiting list | until 05.10.2024 |
