Please note that this course description is preliminary. The final course description will be released in the Summer of 2019.
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
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
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
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.
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, Deep Learning, Nature 2015.
Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press (2017); available from: http://www.deeplearningbook.org/
Selected papers from:
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.
Lecturer: Prof.dr. S.F. (Simon) Portegies Zwart
Assistant: Sander Schouws