https://github.com/princetonuniversity/intro_machine_learning
https://github.com/princetonuniversity/intro_machine_learning
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/princetonuniversity/intro_machine_learning
- Owner: PrincetonUniversity
- Created: 2023-01-13T19:46:06.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-26T02:57:22.000Z (over 1 year ago)
- Last Synced: 2024-06-07T19:43:12.915Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 58.2 MB
- Stars: 16
- Watchers: 6
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Introduction to Machine Learning
## About
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 a hackathon during Part 5 where participants will work on a small, end-to-end machine learning project chosen from one of multiple domains (e.g., computer vision, natural language processing).
Attendees should have some familiarity with Python and basic calculus.
## Live Mini-Course
The [Introduction to Machine Learning](https://cglink.me/2gi/r1938768) mini-course will be held during [Wintersession 2024](https://winter.princeton.edu) on January 16, 17, 18, 22, 23 in Lewis Library 120 at 2:00-4:00 PM.
## Day 5 Hackathon
- Computer vision: Learn more about CNNs, classify dogs versus cats using a simple CNN, and use transfer learning with an advanced CNN (ResNet-50) to classify dogs versus cats.
- Diffusion models: Learn about diffusion models (e.g., DALL-E 2) then build one and train a generative model for images.
- Large Language Models: This session introduces the basics of language modeling using the transformer architecture. Participants will learn how to download and fine-tune an LLM using the Hugging Face library.
## Colab Not Working?
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](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.
## Authorship
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.