AI in the Scientific Loop

Who we are

• Monday and Wednesday, 8:00–9:20: Brian Yurk
• Tuesday and Thursday, 1:30–2:50: Catie Welsh

Both sections use the same materials and the same course site.

What this course is about

Generative AI is changing how scientists find information, analyze data, write, and code. By the end of the semester you will be able to

• explain how generative AI works and where its limits come from
• check what it gives you, and use it responsibly
• use it as part of scientific work
• decide when it should and should not be used

No programming or AI experience is required.

How the semester goes

1. Python, and how to use AI responsibly while you learn it
2. How AI works, from learning from data to large language models
3. AI in scientific practice, with student-led discussions and a final project

What you will do

• Labs, Python first, then models, then generative AI
• Discussions, where you read before class and write before we talk
• AI in your field, about how AI is used in a field you care about
• A midterm data-analysis project, on your own
• A final project, in groups of three

The syllabus has the details and the grade weights.

AI in this course

• 🟢 Labs, AI in your field, and the final project. Use AI when an assignment says so, and add a note on how you used it.
• 🟡 Preparing for discussions. AI can help you understand a reading, but it does not write for you.
• 🔴 In-class assessments and the final presentation, where AI is not allowed

For the first weeks you write the code yourself.

The whole policy is on the course site.

Where things are

• The course site: the schedule, the Python tutorials, the discussions, and the policies
• Moodle: anything you hand in with your name on it, and your grades
• GitHub: your labs, starting in week 2

Before next class