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Learn

Explore a library of educational materials (workshops, guides, books, videos, etc.) covering a wide range of ML-related topics, tools, and workflows. From foundational concepts to advanced techniques, these materials offer clear explanations, practical examples, and actionable insights to help you navigate the complexities of ML with confidence.

National Research Platform (NRP)

Free open-weight LLM endpoints plus free Kubernetes GPU compute for InCommon-affiliated researchers. Reference + Python code-along for hitting the NRP-hosted LLM API.

Claude Code Best Practices

AI coding agents — tools that can autonomously write, edit, run, and test code on your behalf — are rapidly changing how software gets built. The space is crowded and…

Understanding Quantization and Precision

Explore quantization and floating-point precision in deep learning — covering FP32, FP16, BF16, INT8, and 4-bit formats and their impact on GPU memory and inference speed.

Intro to GCP for Machine Learning & AI

This Intro to GCP workshop teaches core workflows for building, training, and tuning ML/AI models in Google Cloud’s Vertex AI platform. Participants learn to set up data…

UW-Madison Cloud Services (AWS, GCP, Azure)

UW-Madison offers enterprise cloud computing through contracts with Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. These services are managed…

OpenScholar: Scientific Literature Synthesis with Retrieval-Augmented LMs

Foundation models

OpenScholar is an open-source, retrieval-augmented language model (LM) designed to help researchers navigate and synthesize scientific literature. Developed by the Allen…

Claude Code Cloud Setup Guide (Vertex AI & Bedrock)

This guide walks UW-Madison researchers and staff through setting up Claude Code with a cloud provider — either Google Vertex AI or Amazon Bedrock. It covers Windows and…

Intro to AWS SageMaker for Predictive ML/AI

This introductory AWS SageMaker workshop teaches core workflows for running predictive ML/AI models in AWS SageMaker, an AWS-managed machine learning environment.…

Google Colab

Google Colab is a cloud-based Jupyter notebook environment that runs entirely in the browser. It allows you to write and execute Python code without installing anything…

BadgerCompute

BadgerCompute is UW–Madison’s browser-based interactive computing service built on JupyterHub. It provides on-demand access to CPUs, memory, and GPUs without requiring any…

GeoDeepDive: Unlocking Knowledge from Scientific Literature

A guide to using GeoDeepDive, a powerful platform and database for large-scale text and data mining of scientific documents.

Computational and Inferential Thinking: The Foundations of Data Science

A beginner-friendly introduction to data science, statistics, and Python programming through hands-on exercises — no prior experience assumed.

Peter Cruz Parrilla

Interpretable Machine Learning

Interpretability

A comprehensive guide to understanding and explaining ML models, covering model-specific and model-agnostic interpretation methods.

Peter Cruz Parrilla

MLflow 101: Local Experiment Tracking for Beginners

Reproducibility

This is a beginner-friendly overview of local experiment tracking with MLflow. If you’ve ever lost a “good” result because you forgot which settings you used, MLflow solves…

Exploring Fact-Based QA with RAG: Romeo and Juliet

Prompt engineering

Build an end-to-end RAG pipeline from scratch — covering text chunking, semantic embeddings, retrieval, and LLM-based answer generation.

Learning Through Comparison: Use Cases of Contrastive Learning

Contrastive learning

Multimodal learning

Representation learning

Contrastive learning is reshaping how models learn, driving widespread progress in feature learning, clustering, out-of-distribution detection, and multimodal applications.…

Yin Li, Chris Endemann

UW Generative AI Services & Policies

Microsoft Copilot

Foundation models

The University of Wisconsin–‍Madison is committed to responsibly harnessing the power of generative artificial intelligence to enhance teaching, learning, research and…

Introduction to Statistical Learning

Unsupervised learning

A foundational introduction to statistical learning covering regression, classification, tree-based methods, and deep learning with R and Python labs.

One Useful Thing

Industry applications

One Useful Thing is a blog where Wharton professor Ethan Mollick “provides a research-based view on the implications of AI.” Since November 2022 Mollick has been sharing a…

NotebookLM: A GenAI Summarization Tool

Foundation models

NotebookLM is a generative AI tool designed to assist with understanding dense, complex texts, including research papers in machine learning and AI. Developed by Google Labs…

Trustworthy AI - Explainability, Bias, Fairness, and Safety

Interpretability

Computer vision

The Trustworthy AI: Explainability, Bias, Fairness, and Safety workshop equips participants with practical skills to evaluate and improve the trustworthiness of machine…

Exploring the Titanic Dataset

A hands-on EDA tutorial covering data cleaning, visualization, outlier detection, correlation analysis, and feature engineering with the classic Titanic dataset.

Grokking

The verb, “to grok”, was originally coined by Robert A. Heinlein in his 1961 science fiction novel “Stranger in a Strange Land,” where it meant to understand something so…

Intro to Python

The Plotting and Programming in Python workshop provides an introduction to programming in Python 3 for people with little or no previous programming experience. It uses…

Intro to Machine Learning with Sklearn

Unsupervised learning

The Intro to Machine Learning with Sklearn workshop will walk you through introductory machine learning concepts as well as how to implement common ML methods (e.g.…

Intro to Deep Learning with Keras

The Intro to Deep Learning with Keras workshop will walk you through introductory deep learning concepts as well as how to build a neural networks in Keras. Keras is…

Intro to Deep Learning with PyTorch

The Intro to Deep Learning with PyTorch workshop from Udacity will walk you through introductory deep learning concepts as well as how to build a neural networks in PyTorch.…

Understanding Deep Learning

A modern, theory-focused deep learning textbook covering neural networks, CNNs, transformers, generative models, and reinforcement learning with PyTorch notebooks.

Intro to Text Analysis / NLP

The Intro to Text Analysis workshop introduces the field of Natural Language Processing (NLP) and how to gain insights from collections of text data (i.e., a corpus). This…

Overview of Reproducibility Lecture

Reproducibility

Dr. Sarah Stevens’ lecture highlights the critical importance of reproducibility in computational and data science projects. She also shares best practices to ensure…

Version Control with GitHub Desktop

Reproducibility

Navigating the world of version control systems like Git can initially feel daunting, especially for those new to programming or collaborative software development projects.…

Version Control with Git and GitHub

Reproducibility

The below video (and the 7 subsequent videos in the workshop playlist) will walk you through this introductory Git workshop from the Carpentries: Version Control with…

Intro to Out-of-Distribution Detection

The below tutorial from Sharon Li, an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, introduces a pervasive problem faced…

How to Contribute?

Nexus is built by people sharing what they’ve learned with each other. This guide covers what we’re looking for, what makes a good contribution, and how to submit one.

Center for High Throughput Computing (CHTC)

Established in 2006, the Center for High Throughput Computing (CHTC) is UW-Madison’s core computational service provider for large-scale computing. CHTC offers two main…