
Deep Learning
Short or summer course

This one-week Deep Learning course covers theoretical and practical aspects, state-of-the-art deep learning architectures and application examples. The lectures will introduce to students the fundamental building blocks of deep learning methods and the weaknesses and strengths of the different architectures. Students will learn how to tailor a model for a particular application. During tutorials students practice the theory using exercises and have the opportunity to ask for additional explanation for those parts of the material perceived as more difficult. Computer lab sessions aim at making the material come alive and train students in how the methods learnt in class can actually be applied to data. The lab sessions are meant to work on the assignments, such that the students automatically keep up with the material.
Topics covered
- Introduction to Deep Learning (High-level definitions of fundamental concepts and first examples)
- Deep Learning components (gradient descent models, loss functions, avoiding over-fitting, introducing asymmetry)
- Feed forward neural networks
- Convolutional neural networks
- Embeddings (pre-trained embeddings, examples of pre-trained models, e.g., Word2Vec)
- Generative Adversarial Network (GAN)
- Advanced architectures (Densely connected networks, Adaptive structural learning)
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Visit course websiteLanguage
English
Title
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Duration
5 days
ECTS credits
ECTS
The European Credit Transfer and Accumulation System (ECTS) is a student-centred system based on the student workload required to achieve the objectives of a programme of study. Its aim is to facilitate the recognition of study periods undertaken by mobile students through the transfer of credits. The ECTS is based on the principle that 60 credits are equivalent to the workload of full-time student during one academic year.
Accreditation
Information not available
Tuition fee 2025/2026
EU/EEA
The EU/EEA rate is the regular fee for students from within the EU/EEA.
€ 1,000
Admission
Application requirements
Admission requirements
Students are expected to have a solid background in calculus, linear algebra, and classical statistics. Familiarity with open source languages such as R or Python is a must.
Level
The summer course welcomes Master’s and PhD students, alumni, professionals in economics and related fields, who are interested in deep learning. The level is introductory, targeted at participants who would like to familiarize themselves with the topic, and acquire a good basis from which to approach deep learning potential applications.
Check when you can start and what you have to pay!
Tuition fees | |
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€ 1,000 | |
Non-EU/EEAThe non-EU/EEA rate is the rate for students from outside the EU/EEA. |
Information not available |
InstitutionalThe institutional rate is for all students who have already obtained a bachelor’s or master’s degree and who want to start a second programme leading to a degree at the same level or at a lower level. |
Information not available |
Start date | App. deadline EU/EEA | App. deadline Non-EU/EEA |
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30 Jun '25 | 16 Jun '25 | 16 Jun '25 |
Contact
Main addressDe Boelelaan 1105
1081 HV Amsterdam
020-5985020
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Visit course website
