Machine Learning Workshop
Information for Attendees
Here you find all information about the workshop, including the program and how to prepare.
If you have additional questions, please contact us by replying to the email that was sent to you.
Venue and Dates
This workshop is held remotely on Zoom as part of the AAS #237. The workshop takes place on January 7 and 8 2021 from 12-3pm ET.
A Slack channel will be provided by the AAS staff on which questions can be asked in parallel to the Zoom session
How it works
During the workshop, we will go through tutorials that are presented in several Jupyter Notebooks. All code is run on Amazon Web Service (AWS) instances, hence you do not have to install any code or packages! The IP address for your instance together with password and user name will be distributed the day before the workshop. If you are a Windows user, you need to install some software to be able to access AWS. Please try to log in to your AWS instance the day before the workshop and let us know if you have questions.
Program (times in ET)
The day before (January 6)
We will send you the AWS log-in information the day before the start of the workshop. Please try to log in to your AWS instance. If you find problems or you have questions, let us know immediately so we can try to solve the issues. Unfortunately, there will be not much time during the workshop to trouble shoot, therefore join Zoom 15-30 minutes before the workshop starts if you are still not able to access your AWS instance.
Day 1 (January 7, times in ET)
11.30am: Zoom connection should be running 30 minutes before the Workshop. Please log on early so we can start on time. If you cannot access your AWS instance (please try it out before the workshop!), let us know so we can try to solve the issue.
12.00pm: Welcome
12.15pm: Introduction to Convolutional Neural Networks (CNN) via a linear regression example (led by Asad Khan) - [Tutorial_1]
12.45pm: CNN example on image-based galaxy morphology classification (led by Sinan Deger) - [Tutorial_1]
1.15pm: Random Forest example on catalog-based galaxy morphology classification (led by Sinan Deger) - [Tutorial_1]
1.45pm: Unsupervised Machine Learning (SOM, T-SNE) explained on image-based galaxy morphology classification (led by Andreas Faisst) - [Tutorial_1]
2.15pm: Visualization of CNNs (led by Asad Khan) - [Tutorial_1]
2.45pm: Q&A: Time for more questions and explanations
3.30pm: End of Day 1 (Zoom closes)
Day 2 (January 8, times in ET)
12.00pm: Welcome back!
12.05pm: Example of Random Forest to reduce Spitzer IRAC data (led by Jessica Krick) - [Tutorial_2]
1.05pm: Photometric Redshifts and Galaxy Properties with SOMs (led by Dan Masters) - [Tutorial_3]
1.35pm: Deriving the spin of black holes via gravitational waves with Bayesian Neutral Networks (led by Hongyu Shen and William Wei) - [Tutorial_4]
2.15pm: Q&A: Do you want more details on a specific tutorial? Are you planning to use ML for your own works? This is the time to discuss your questions with ML experts!
3.00pm: Wrap up and final remarks
3.30pm: End of Day 2 and end of workshop (Zoom closes)