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Deep Learning

Course are scheduled to start on May 6. Course location: Zuidas Amsterdam. 

Deep Learning

Registration for this course

Deadline for registration is April 19, 2020. Go the the registration pageMax. capacity: 20 students. 

Course Schedule

Courses are scheduled on Wednesdays, in the afternoon, location Zuidas Amsterdam. First course starts on May 6, the exam will be scheduled in week 28.

Course Content

The Deep Learning course covers theoretical and practical aspects, state-of-the-art deep learning architectures, and application examples.

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., GloVe embeddings, Word2Vec)
  • Recurrent neural networks
  • Long-short term memory units
  • Advanced architectures (Densely connected networks, Adaptive structural learning)

Learning Objectives

By the end of the course students will be able to:

  • Understand the fundamental building blocks of deep learning methods,
  • understand the weaknesses and strengths of the different architectures,
  • know how to tackle weaknesses and tailor the model for a particular application,
  • program these methods, and
  • be able to describe the numerical computational steps applied by the machine.


Coordinator/Lecturer: dr. Eran Raviv (APG-AM)

Course Fees

Course fee for internal research master and PhD students: € 1.000,-
Course fee for external PhD students: € 1.500,-

Entrance Requirements

Recommended Knowledge: Machine Learning
Required Knowledge: Linear algebra, Regression

Link to course manual


Registration for this course

Go to the registration page