Are you a Bayes fan but your model is just not up to the task because with great power comes great computing power consumption as well? Look no further; in this course, we will showcase how one can easily construct a neural network to rapidly emulate summary statistics from almost any complex model. We will also demonstrate how to write a simple Bayesian framework to perform inference with the emulator.
If you have any questions regarding this training, please contact email@example.com.
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. Training data can be anything you would like to emulate of your models, such as a forward-modelled 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.
NCI is developing a series of AI/ML course with experts from various disciplines. This training is designed to be the first course 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.
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.
- Models in astronomy (CMB, 21-cm, IGM neural fraction, Galaxy population)
- AI/ML in astronomy
- Basics of neural network
- 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