Functional Open Science Skills for AI/ML Applications
When
This session covers critical concepts like data splitting (training, validation, and test sets), evaluating model performance, and hyperparameter tuning. Participants will explore common pitfalls and best practices for achieving reliable results, using concepts and code developed in previous sessions.
This workshop series provides graduate students in public universities with developing skills and learning tools required in today's AI/ML-focused science.
Ranging from covering the basic moving parts to understanding AI's role in Open Science, this workshop aims to lend an understanding where to obtain compute, covering software environments and reproducibility, the role of workflows, and aiming to create an end-to-end Machine Learning (ML) workflow.
SERIES: Functional Open Science Skills for AI/ML Applications
Where: Register for Zoom Link
Instructor: Michele Cosi and Carlos Lizárraga
YouTube: UArizona DataLab and session links
- 01/28 The moving parts of Functional Open Science
- 02/04 AI's Role and Tools in Open Science
- 02/11 Learning to Work in the Cloud: JetStream2 and Reproducibility
- 02/18 Handling Images & Videos pt. 1
- 02/25 Handling Images & Videos pt. 2
- 03/04 Training and Testing Models
- 03/18 End-to-end ML Workflow pt.1
- 03/25 End-to-end ML Workflow pt.2