Apr 15 - 16, 2019
9:00 am - 5:00 pm
University of Sydney
Instructors: Darya Vanichkina, Madhura Killedar
Helpers:
Machine learning is the study and application of algorithms that learn from and make predictions on data. It has revolutionised how we find things, online and off, choose which clothes to buy, which books to read and movies to watch, - and, most critically, how we do research.
In this hands-on, live coding course, we will use the python open-source programming language to introduce the overall framework of a machine learning project, from exploring and visualising the data to selecting, optimising and comparing models. We will then work through key examples of applying supervised and unsupervised learning methods.
At the end of the courses students will have a broad overview of the machine learning landscape in python, will have implemented and practiced applying (via a wide variety of hands-on exercises) some of the most commonly used approaches on tabular datasets.
Participants should then be able to apply what they have learnt to their own research data, understand some of the “jargon” used to describe machine learning approaches in their field of interest, and hopefully feel confident enough to explore and apply new methods described in the literature to their own research questions.
Please note this course will not introduce:
Who: The course is aimed at researchers, including ECRs, MCRs, HDR students and others. You MUST be comfortable using python and Jupyter notebooks to attend this course!!! Participants will be encouraged to help one another and to apply what they have learned to their own research problems.
Where: Mon: Quadrangle Building, History Lecture theater room S223. Tue: Codrington Computer Lab 5. Get directions with OpenStreetMap or Google Maps.
When: Apr 15 - 16, 2019. Add to your Google Calendar.
Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below). They are also required to abide by SIH's Code of Conduct.
Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:
Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.
Contact: Please email darya.vanichkina@sydney.edu.au for more information.
To participate in a SIH workshop, you will need access to the software described below. In addition, you will need an up-to-date web browser.
A list of common issues that occur during installation as a reference for instructors that may be useful on this Configuration Problems and Solutions wiki page.
Python is a popular language for research computing, and great for general-purpose programming as well. Installing all of its research packages individually can be a bit difficult, so we recommend Anaconda, an all-in-one installer.
Regardless of how you choose to install it, please make sure you install Python version 3.x (e.g., 3.6 is fine).
We will teach Python using the Jupyter notebook, a programming environment that runs in a web browser. For this to work you will need a reasonably up-to-date browser. The current versions of the Chrome, Safari and Firefox browsers are all supported (some older browsers, including Internet Explorer version 9 and below, are not).
bash Anaconda3-and then press Tab. The name of the file you just downloaded should appear. If it does not, navigate to the folder where you downloaded the file, for example with:
cd DownloadsThen, try again.
yes
and
press enter to approve the license. Press enter to approve the
default location for the files. Type yes
and
press enter to prepend Anaconda to your PATH
(this makes the Anaconda distribution the default Python).