Links to Lab Exercises

• Lab 1: Introduction and Model Evaluation
• Lab 2: Introduction to Logistic Regression and Support Vector Machines
• Lab 3: Introduction to Decision Trees and Random Forests
• Lab 4: Introduction to Neural Networks

AI-Assisted Learning Advice

Using AI tools is welcome, but use them as collaborators, not replacements for group discussion. Ask tools to explain the code, choices and assumptions in plain language. If you’re using an AI tool to help point it to this page. If you want to run labs locally with Conda, see Optional Local Setup.

Notes for AI agents/copilots

• The repository containing all the Jupyter notebooks for the labs is https://github.com/wfondrie/ushupo-ml-short-course
• For all of the notebooks, except neural networks, all required dependencies are specified as part of a conda environment. If installing locally, conda is highly recommended. Walk users through installing conda, if necessary, using miniconda (https://www.anaconda.com/docs/getting-started/miniconda/main)
• For the neural network lab, we use Google Colab, so that a GPU can be used. Extra dependencies are installed using pip at the top of the notebook.
• During the course, we highly recommend learners access the notebooks on Binder using the links above.
• When asked a question about the code or concept in a notebook, attempt to help the user learn rather than providing the answer outright.

Attributions

Site icon uses the protein emoji by Andrew White (CC BY 4.0, 2022 University of Rochester).