X-Sensing 2019

Cross-disciplinary Conference on Scientific Analytics.

25 - 29 November 2019, Coffs Harbour, Australia.

 

Hosts

MQ Astro DPI NSW AAO-MQ
Python code

Training Workshop

Monday and Tuesday will host tutorials in data-science methods and machine learning. We will be covering the topics listed below at an intermediate level, mostly using Python.

The idea here is to provide robust and fully-functioning examples that work on scientific data (i.e., not just finding cats in images!). The code will be well-documented and open-source. We will take you through it step-by-step and you can dig deeper at your leisure.

Bring Your Own Data

Do you have a problem that could benefit from the methods covered here? Then please bring your data to the workshop! Where practical, the organisers will help you fit your data into the necessary frameworks.

Topics

The following topics will be covered:

Introduction to machine learning

This tutorial introduces machine learning and teaches how to apply regression, clustering analysis and deep-learning to simple problems. The lesson is inspired by Software Carpentry and conducted through live-coding using python.

Prerequisites: a basic understanding of python, including for-loops, if-statements, using functions and basic arithmetic.

Dynamic Dashboards and Visualisation Using Python

This tutorial covers how to build a ‘full stack’ web dashboard to visualise data in real-time. Technologies include: plotly dash, Flask, RESTful APIs, SSL for encryption and deploying on Linux or AWS. The lesson aims to present a recipe and modular code for building a complete application that can be re-purposed for your own work.

Prerequisites: an intermediate understanding of python for data science, including basic pandas and matplotlib.

Finding Structure in Data

This lesson presents practical examples of finding and measuring structure in multi-dimensional data. Methods include cluster-finding using K-means and Gaussian Mixture Models.

Prerequisites: an intermediate understanding of python for data science, including pandas and matplotlib.

Practical Deep-Learning for Science

Convolutional Neural Networks (CNNs) have revolutionised the field of computer vision and are increasingly used for scientific analysis. This lesson shows how to use CNNs for scientific data analysis, leveraging examples from astronomy.

Prerequisites: a basic understanding of python, including for-loops, if-statements, using functions and basic arithmetic.