Kies de Nederlandse taal
Course module: JM1100-M-6
Deep Learning
Course info
Course moduleJM1100-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionDutch
Offered byTilburg University; Tilburg School of Economics and Management; TISEM Other;
Is part of
M Data Science and Entrepreneurship (joint degree)
prof. dr. E.O. Postma
Other course modules lecturer
Academic year2018
Starting block
SM 2
Course mode
RemarksThis information is not up to date. Check the Course Catalog 2019 or select the course via “Register”.
Registration openfrom 10/01/2019 up to and including 31/07/2019
Doel & Inhoud Engels
The objective of the course is to provide students with: (1) theoretical knowledge of convolutional and recurrent neural networks, (2) practical skills for performing experiments with deep learning, (3) understanding ways to (pre-)process signals, images, and texts for data science applications in general and for deep learning specifically.

Deep Learning revolutionized machine learning by yielding the best performances in a large variety of application domains such as: speech recognition, image recognition, object detection, drug discovery and genomics. This course provides students with the understanding and skills to apply deep learning to signals, images, videos and textual sources. The course includes a training to run deep learning algorithms on special hardware.

Recommended Prerequisites
Basic understanding of machine learning / data mining. Elementary Statistics
The lectures of the course start with a historical overview of deep learning. After a review of the formal basics, deep feedforward networks are explained in terms of (nonlinear) transformations and the backpropagation training procedure. Subsequently, the importance of regularization to prevent overfitting is explained and procedures for optimizing the induced model are outlined. Then, the notions of convolution and convolutional neural networks are explained. Sequence learning is addressed next by means of recurrent neural networks and their applications. This is followed by a review of the practical methodology of deep learning methods. Finally, applications are reviewed and recent scientific progress on the development of deep learning methods is discussed.
During skill classes, students are trained on applying the concepts addressed in the lectures.

Type of instructions
lab sessions; self study; MOOC session (occasionally)

Type of exams
Presentation 20% and Paper 80%. There will be interim tests with which students can earn bonus points (to a max. of 20%)

Compulsory Reading
  1. Goodfellow, I., Bengio, Y. and Courville, A., Deep Learning, Cambridge MA: MIT Press, 2016.
Timetable information
JM1100-M-6|Deep Learning
Written test opportunities
Schriftelijk / WrittenEXAM_01SM 2208-07-2019
Written test opportunities (HIST)
Schriftelijk / WrittenEXAM_01SM 2103-06-2019
Required materials
Recommended materials

Final grade

Kies de Nederlandse taal