AI-Assisted Coding and Modern Workflows ONLINE

Ziele

AI-powered tools demonstrate new and exciting possibilities in a great variety of areas. A particularly promising direction is AI-assisted programming. This course introduces you to the rapidly evolving world of AI-powered tools and novel programming workflows. We will start by exploring different applications, such as working with documents and organising knowledge, generating visuals and editing images, and programming, among others. You will gain an intuitive understanding of the underlying mechanisms for creating (training) these tools and where strengths and limitations lie. The focus of this course will be on AI-assisted coding, that is, assisted translation of your ideas into the programming language Python. You will learn about essential programming tools like Git, GitHub, the command-line interface, and integrated development environments. Through guided exercises, you will progress from AI-assisted coding, i.e. extending code snippets in existing scripts, to “vibe coding”, i.e. fully generating scripts from natural language instructions. By the end, you will be equipped with practical skills to start integrating AI tools in your own research and programming workflows.

Kursinhalte

We will explore the following topics:

• Overview of different AI tools with explicit examples for areas like working with documents, research, creating visuals/images, and with a focus on programming in Python
• Explanation of the high-level training mechanisms of AI tools and what works well or less well
• Introduction to the classical programming toolset covering Git, the command-line interface (CLI), and integrated development environments (IDEs) like VS Code
• Programming from AI-assisted coding to “vibe coding” with GitHub Copilot

The course will showcase tools like NotebookLM, ChatGPT, Claude, Adobe Firefly, and OpenAI Codex. As the landscape of AI tools rapidly evolves, the specific choice of tools is subject to change.

Form

A key element of the course is the instructor’s live presentation of different AI tools and use cases. Participants will have the opportunity to explore, modify, and experiment with provided scripts.

Course material will be made available in advance, and participants can use their own computers during the course days. Instructions on setting up a Python environment, the integrated development environment (IDE) VS Code, and GitHub Copilot will be shared beforehand. For the best experience, a dual-monitor setup is recommended (one screen to follow the presentation and another to work with the course material).

Adressatinnen und Adressaten

All Doctoral Candidates & Postdocs

Voraussetzungen

For the programming part:

• A solid foundation in Python is an advantage.
• Participants will need to set up Python, install an integrated development environment such as VS Code, and subscribe to GitHub Copilot (which is freely available to PhD students and Postdocs). Instructions for this will be provided prior to the course.

Informationen zu den Dozierenden

Maxim Samarin is a Senior Data Scientist at the Swiss Data Science Center. Maxim holds a PhD in Computer Science / Machine Learning and has more than nine years of experience as a researcher in Machine Learning.

Leistungsspektrum / Workload

Course attendance 8h, preliminary work 3h

Besonderheiten

Once registration is open, applications will be collected for 24 hours and course places allocated by lot. All registrations received after the initial 24h period will be put on a waiting list and assigned on a first come, first served basis.

Course places/places on the waiting list will be confirmed by e-mail. Course registrations can only be canceled before the registration period ends (send an e-mail to grace@unibas.ch). Full course attendance is mandatory. Participants who fail to attend a course without prior notification or withdraw after the registration deadline are subject to a fee of CHF 30. In addition, participants who cancel their course registration at a later point in time, are absent without an excuse or do not attend the entire course will, for reasons of fairness, not be considered for course registration in the following semester and will be removed from other courses offered in the same semester. Please find the detailed regulations on the Transferable Skills Homepage.