Machine Learning: practical application to Astrophysics

16 Sep 2020, 09:00 → 17 Sep 2020, 15:00 Europe/Berlin

VC (AIP)

Gal Matijevic

2-day Machine Learning workshop

with focus on practical application of diverse
methods to problems in modern astronomy.

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Costs will be shared by the participants.

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Survey-Results

SurveyResults.pdf

Wednesday, 16 September

09:00 → 10:30 Introduction to Machine Learning

•Introduction: different types of machine learning algorithms, theoretical background, practical aspects, structure and elements of (convolutional) neural networks

10:30 → 10:45 Coffee Break

10:45 → 12:15 Introduction to Machine Learning: Unsupervised Learning

•Introduction: different types of machine learning algorithms, theoretical background, practical aspects, structure and elements of (convolutional) neural networks

12:15 → 13:15 Lunch Break

13:15 → 15:00 Introduction to Machine Learning: Supervised learning I

•Introduction: different types of machine learning algorithms, theoretical background, practical aspects, structure and elements of (convolutional) neural networks

Thursday, 17 September

09:00 → 10:30 very deep neural networks: Supervised learning II

use of generative adversarial networks (GANs) for generation of images of galaxies with constraints - semi-supervised case (example: We want computer to generate an image of a certain type of galaxy. The image is close to indistinguishable from an observed image)

10:30 → 11:00 Coffee Break

11:00 → 12:30 Supervised Learning III: generative adversarial networks

use of generative adversarial networks (GANs)

12:30 → 13:30 Lunch Break

13:30 → 15:00 very deep neural networks: generative adversarial networks

use of generative adversarial networks (GANs) for generation of images of galaxies with constraints - semi-supervised case (example: We want computer to generate an image of a certain type of galaxy. The image is close to indistinguishable from an observed image)