Astro AI

Applying Machine Learning Methods to Astronomy

Benjamin Moster

Wintersemester 2020/21

I will give an introduction to modern machine learning methods, such as Neural Networks / Deep Learning, Decision Trees / Random Forests, Support Vector Machines and Gaussian Mixture Models. The aim of this course is not to cover these topics comprehensively or in great theoretical detail, but to show how machine learning methods can be used in astrophysical research. Further we will focus on Feature Importance and Probability Calibration.

Topics that will be covered:

Lecture: Mondays 2 c.t. (14:15) @ Zoom virtual lecture room
Tutorial: Mondays 10:00 Zoom



Introduction (Nov 2)
As the lecture was announced only late (sorry), we will have a short meeting, where I will show how AI is currently used in Astronomy. The first lecture will then be the week after (Nov 9).

Zoom Link
Slides


Lecture 1 (Nov 9)
topics:

Zoom Link
Slides
Exercise Sheet 1
Jupyter Notebook
Jupyter Notebook - Solution




Lecture 2 (Nov 16)
topics:

Zoom Link
Slides
Exercise Sheet 2
Jupyter Notebook




Lecture 3 (Nov 23)
topics:

Zoom Link
Slides
Exercise Sheet 2



Anaconda

We will use Jupyter Notebooks for most if not all of the exercises. For this I recommend the installation of an Anaconda distribution. This not only includes Python and Jupyter, but also almost all libraries that we will need. We will use Python 3, so please use the according Anaconda distribution. You can download it from here:

Anaconda Download Website



scikit-learn

We will use scikit-learn for most ML problems (except for Neural Networks). You can install it through pip:
pip install scikit-learn

scikit-learn



Tensorflow

Later in the course we will use Tensorflow for Deep Learning. You can learn more about it here:

Tensorflow