using Dates
utc_now = now(UTC)
aest_now = utc_now + Hour(10)
println("Last built: ", Dates.format(aest_now, "dd-mm-yyyy HH:MM"), " (AEST)")Last built: 24-06-2025 12:04 (AEST)AIMS: Julia Language Workshop by Accumulation Point
Welcome to the AIMS Julia Language Workshop run by Accumulation Point.
Your instructors are Yoni Nazarathy and Aapeli Vuorinen.
This iteration of the workshop runs over three days during June 2025 via videoconferencing. The schedule is as follows:
| Session | Unit | Material | Instructor(s) |
|---|---|---|---|
| Day 1 full day | Unit 1 | Your first (BIG) day with the Julia language | Yoni & Aapeli |
| Day 2 morning | Unit 2 | Processing Data | Aapeli |
| Day 2 afternoon | Unit 3 | Numerics and Monte Carlo | Yoni |
| Day 3 morning | Unit 4 | Parallel and Fast | Aapeli |
| Day 3 afternoon | Unit 5 | Machine Learning, Statistics, and Optimization | Yoni |
GitHub: The course materials are generated (from .qmd files) from the GitHub repo with Quarto. The GitHub repo also defines a Julia environment for each unit of the course. It is recommended to clone (or download the zip) of the repo and then work in the /work folder under each unit. Then when working on unit X, it is recommended to instantiate the environment for that unit. Instructions on how to do this will be given during the course.
The nature of the course (how it works): The instructors present material live, following the Quarto-generated pages, and demonstrate code live (in VS Code/the REPL/Jupyter/etc). Example notebooks and files created by the instructors will be saved in the /work folder and committed to GitHub. It is recommended that course participants (students) run some of the code in parallel to the instructors, try out different things, and carry out short exercises. Slightly longer exercises are suggested at the end of each unit and these can be carried out by participants between sessions. Online help sessions will also be provided by the instructors. As a first step, participants should install Julia as outlined in Section 1 of Unit 1.
General resources: The course materials link to general resources for specific subjects covered. The following are some other valuable resources for the course as a whole:
- The Julia Language Documentation
- The Julia Language YouTube Channel
- The Jolin.io YouTube Channel
- The Julia Fundamentals Path in Pumas
- The Julia Discourse (forum)
- The Julia language Slack channel
- Julia GitHub Organizations
- Getting help from an LLM is always helpful.
Books: If you are a “book person” you may be interested in the following books: Kochenderfer and Wheeler (2019), Lauwens and Downey (2019), Nazarathy and Klok (2021), and Türeci, Dağıstanlı, and Türk Çakır (2025). The Kochenderfer and Wheeler book is a great optimization resource using Julia code. Lauwens and Downey is a great introductory coding book using Julia, The Nazarathy and Klok book is a statistics book, also introducing Julia. Finally, the newer Türeci, Dağıstanlı, and Türk Çakır seems to be a good resource, especially for readers with a physics background.
We hope you enjoy the course and that we can help you come out of it with an appreciation of the many strengths and advantages of the Julia programming language!
Time of build for this version:
References
Kochenderfer, Mykel J, and Tim A Wheeler. 2019. Algorithms for Optimization. Mit Press. https://mitpress.mit.edu/9780262039420/algorithms-for-optimization/.
Lauwens, Ben, and Allen B Downey. 2019. Think Julia: How to Think Like a Computer Scientist. O’Reilly Media. https://www.oreilly.com/library/view/think-julia/9781492045021/.
Nazarathy, Yoni, and Hayden Klok. 2021. Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. Springer Nature. https://statisticswithjulia.org/.
Türeci, R. Gökhan, Hamdi Dağıstanlı, and İlkay Türk Çakır. 2025. Julia Programming for Physics Applications. Springer. https://link.springer.com/book/10.1007/978-3-031-84716-5.