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Deep Learning
Last Updated: 2026-02-05 16:01:33
Abstract
Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
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.
Resources
Learning Materials (Links)
- Main link
- Information
General Information
- Language
- English
- Levels
- BSC , MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 120 minutes
- Aids
- limited aids (4 x A4 pages of notes)
Registration & Places
- Max Places
- 320
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Deep Learning |
|
3 h weekly |
| exercise | Deep Learning |
|
2 h weekly |
| independent project | Deep Learning | No time listed | 2 h weekly |
Offered In
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Robotics (Only one of the two course units 263-5902-00L Computer Vision resp. 227-0447-00L Image Analysis and Computer Vision may be recognised for credits for the overall (CSE Bachelor and Master) study programmes. Only one of the two course units 263-5210-00L Probabilistic Artificial Intelligence resp. 252-0535-00L Advanced Machine Learning may be recognised for credits in the field of specialisation `Robotics' for the overall (CSE Bachelor and Master) study programmes. However, the other course unit may be recognised for a different category.)
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Robotics (Only one of the two course units 263-5902-00L Computer Vision resp. 227-0447-00L Image Analysis and Computer Vision may be recognised for credits for the overall (CSE Bachelor and Master) study programmes. Only one of the two course units 263-5210-00L Probabilistic Artificial Intelligence resp. 252-0535-00L Advanced Machine Learning may be recognised for credits in the field of specialisation `Robotics' for the overall (CSE Bachelor and Master) study programmes. However, the other course unit may be recognised for a different category.)
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Recommended Elective Courses (These courses are particularly recommended for the Bioelectronics track. Please consult your track advisor if you wish to select other subjects.)
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Application Area (Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area. Credits from other application areas cannot be recognised for further application areas.)
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Machine Learning (The list is not yet complete.)
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Signal Processing and Machine Learning (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Signal Processing and Machine Learning ", see . The individual study plan is subject to the tutor's approval.)
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Core Courses (These core courses are particularly recommended for the field of "Signal Processing and Machine Learning". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT.)
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Advanced Core Courses (Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only.)
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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