This mini-course provides a comprehensive introduction to machine learning. Part 1 introduces the machine learning process and shows participants how to train simple models. Part 2 covers model evaluation and refinement. Artificial neural networks are introduced in Part 3. A survey of different neural network architectures is presented in Part 4. The mini-course concludes with specialized sessions during Part 5 where participants will choose from one of multiple domains (natural language processing, graph neural networks, physical sciences).
Attendees should have some familiarity with Python and basic calculus. This mini-course will be held during Wintersession 2025.
A Hands-On Introduction to Machine Learning
January 15, 16, 17, 21 (2025) at 2:00-4:00 PM
Location: Lewis Library 120
Instructors:
Julian Gold, DataX Data Scientist, CSML
Gage DeZoort, Postdoctoral Research Associate and Lecturer, Physics
Choose one of these options:
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Getting Started with Large Language Models with Princeton Language and Intelligence (Parts 1 & 2)
January 22-23, 2025 at 2:00-4:00 PM
Location: Lewis Library 120
Instructors:
Simon Park, Graduate Student, Computer Science and PLI
Abhishek Panigrahi, Graduate Student, Computer Science and PLI -
Machine Learning for the Physical Sciences
Wednesday, January 22, 2025 at 2:00-3:30 PM
Location: Lewis Library 134
Instructors:
Christian Jespersen, Graduate Student, Astrophysical Sciences
Rafael Pastrana, Graduate Student, Architecture
Quinn Gallagher, Graduate Student, Chemical and Biological Engineering
Holly Johnson, Graduate Student, Electrical and Computer Engineering -
Graph Neural Networks for Your Research
Wednesday, January 22, 2025 at 2:00-4:00 PM
Location: Lewis Library 122
Instructor: Gage DeZoort, Postdoctoral Research Associate and Lecturer, Physics
To prepare for this mini-course, consider attending:
Introduction to Machine Learning for Humanists and Social Scientists (Parts 1 & 2)
January 13-14, 2025 at 10:00 AM-12:00 PM
Location: Arthur Lewis Auditorium in Robertson Hall
Instructor: Sarah-Jane Leslie, Professor of Philosophy and CSML, and NAM Co-Director
Continue learning about machine learning and data science by attending the following:
Introduction to Optimal Transport: Applications to Machine Learning, Cognitive Science, and Comp. Biology
Thursday, January 23, 2025 at 10:30 AM-1:30 PM
Location: Bendheim House 103
Instructors:
Sarah-Jane Leslie, Professor of Philosophy and CSML, and NAM Co-Director
Julian Gold, DataX Data Scientist, CSML
You can run the notebooks for days 1 and 2 of this workshop using only a web browser thanks to jupyterlite.
Step 1: Go to https://jdh4.github.io/intro-ml
Step 2: In the file browser on the left, double click on ML_overview_2024.ipynb for day 1 or Intro_Machine_Learning_Part2_2024.ipynb for day 2 . You can then run the notebook as usual without using Colab or explicitly installing anything. The notebooks will run on your local machine.
The Princeton undergraduate student group, Envision, is hosting a day-long student-focused conference on February 22, 2025. The conference aims to explore the intersection of A.I., information tech policy, and ethics, with the goal of educating, inspiring action, and shaping tomorrow’s leaders.
The materials in this repository were created by Brian Arnold, Gage DeZoort, Julian Gold, Jonathan Halverson, Christina Peters, Jake Snell, Savannah Thias and Amy Winecoff.