Deep Learning

Faculteit Science and Engineering
Jaar 2019/20
Vakcode WMAI18002
Vaknaam Deep Learning
Niveau(s) master
Voertaal Engels
Periode semester II a
ECTS 5
Rooster rooster.rug.nl

Uitgebreide vaknaam Deep Learning
Leerdoelen At the end of this course, the student is able to:
- Explain the theory of deep neural networks
- Explain different methods for optimizing deep neural networks
- Explain the workings of different kinds of networks such as convolutional neural networks and recurrent neural networks
- Explain the workings of generative neural network models
- Train deep neural networks and use them for a particular application
Omschrijving Deep learning receives a lot of attention from the machine learning community nowadays. In this course we will study different deep learning algorithms, starting from training feed-forward neural networks with multiple layers, going though convolutional neural networks and recurrent neural networks and ending with generative models. There will be a practical project where a group of students will work to implement, optimize and test their own deep learning system for a particular application such as object recognition, object detection, speech recognition etc.
Uren per week
Onderwijsvorm Bijeenkomst (S), Hoorcollege (LC), Practisch werk (PRC)
Toetsvorm Presentatie (P), Schriftelijk tentamen (WE), Verslag (R)
(40% Written Exam, 50% Project Report, 10% Presentation. Students need to score at least a 5.0 on the Written Exam.)
Vaksoort master
Coördinator dr. M.A. Wiering
Docent(en) dr. M.A. Wiering
Verplichte literatuur
Titel Auteur ISBN Prijs
Deep Learning Ian Goodfellow, Yoshua Bengio and Aaron Courville 9780262035613 ca. €  80,00
Entreevoorwaarden Prior knowledge is assumed about linear algebra and calculus. Because the course is for AI students, the students are supposed to have this prior knowledge.
Opmerkingen The course unit prepares students to do their graduation project if they choose to do it in deep learning.

This course might only be for Artificial Intelligence / Human-Machine Communication students this year, depending on the number of enrollments. Enrolling to the course unit as a non-AI/HMC student is not a guarantee that you will get in.
Opgenomen in
Opleiding Jaar Periode Type
MSc Artificial Intelligence  (A - General Mandatory Course Units) - semester II a verplicht
MSc Human-Machine Communication  (C - Elective Course Units) - semester II a keuze