Course Information

Prerequisites

Only basic experience with Python is required. Knowledge about Machine Learning and Bayesian inference is sufficient. Training data and practice codes will be provided for the exercises. Attendees are also encouraged to prepare their own training data\footnote{Training data can be anything you would like to emulate of your model, such as a forward-modeled observation that you would like to compare against, or a light cone produced by your 3D simulations.}. The training will focus on using TensorFlow for network training and EMCEE for Bayesian inference. 

The training session is driven on the Australian Research Environment (ARE) and Gadi. Attendees are encouraged to review the following page for background information.

• ARE User Guide
• Gadi

Objectives

This course is designed to be the first one for astronomers. As such, it aims to help attendees

• raise awareness of different applications of AI/ML in astronomy,
• construct a neural network to emulate a complex model,
• build a simple framework to perform Bayesian inference.

Learning Outcomes

At the completion of this training session, you will

• know of a number of AL/ML applications in astronomy,
• bring home a plug-in neural network that you can apply to emulate your own model,
• bring home a plug-in Bayesian framework that you can apply to perform inferences on your own data.

Topics Covered

• Models in astronomy (CMB, 21-cm, IGM neural fraction, Galaxy population)
• AI/ML in astronomy
• Basics of neural network
• neurons
• activation function
• types of neural networks
• training neutral networks
• Data preparation (using the recent 21-cm inference work as demonstration, https://arxiv.org/abs/2108.07282)
• Write and train a neural network step by step
• Write and perform a Bayesian framework step by step