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263-3210-00L 8 Credits BSC , MSC , WBZ D-MAVT , D-INFK , D-MATH , D-PHYS , D-GESS , D-ITET , D-HEST

Deep Learning

Lecturers & Examiners: Prof. Dr. Fernando Perez Cruz, Prof. Dr. Guillaume Romain Obozinski
VVZ CR 2.57

Last Updated: 2026-06-03 00:07:31

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.

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)
Digital
The exam takes place on devices provided by ETH Zurich.
There is a mandatory project (40 hours).Grade = 0.7 exam + 0.3 project

Registration & Places

Max Places
320

Course Components

Type Title Time & Place Hours
lecture Deep Learning No time listed 3 h weekly
exercise Deep Learning No time listed 2 h weekly
independent project Deep Learning No time listed 2 h weekly

Offered In