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Deep Learning in Astronomy


Please note that this course description is preliminary. The final course description will be released in the Summer of 2019.

Admission requirements

  • Bachelor's degree in Astronomy and/or Physics

  • Fluency in Python and Linuxs

  • Experience with processing big data sets


During the course students will be introduced to the field of Deep Learning and its possible applications to astronomy. Next, working in small groups, students will apply Deep Learning to some (open) problems.


  • Basics of machine learning: classification, regression, clustering, overfitting, regularization

  • Multi-layer Perceptron and Backpropagation, Stochastic Gradient Descent, Dropout

  • Convolutional Networks for image classification

  • Recurrent Networks for modelling sequential data

  • Autoencoders for dimensionality reduction and extracting features from data

  • TensorFlow, Keras and GPU-computing

Course objectives

  • Learning basics of deep learning and its possible applications in astronomy

  • Developing practical skills for designing and training deep networks

  • Demonstration of newly acquired skills by solving some astronomy related problems

Soft skills

In this course, students will be trained in the following behaviour-oriented skills:

  • Problem solving (recognizing and analyzing problems, solution-oriented thinking)

  • Analytical skills (analytical thinking, abstraction)

  • Creativity (resourcefulness, lateral thinking)

  • Collaboration (extreme programming, joined research)


See Astronomy master schedules

Mode of instruction

  • Lectures

  • Practical classes

  • student's presentation

Assessment method

  • Project (70%)

  • Presentations (30%)


Blackboard will be used to communicate with students and to share lecture slides, homework assignments, and any extra materials. You must enroll on Blackboard before the first lecture. To have access, you need a student ULCN account.

Reading list

  • Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, Deep Learning, Nature 2015.

  • Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press (2017); available from:

  • Keras documentation

  • Selected papers from:

  • Papers Reading Roadmap

  • Papers Explained


Via uSis. More information about signing up for your classes can be found here. Exchange and Study Abroad students, please see the Prospective students website for information on how to apply.

Contact information

Lecturer: Prof.dr. S.F. (Simon) Portegies Zwart
Assistant: Sander Schouws