Exploring Artificial Intelligence

I am an assistant teaching professor at the University of Notre Dame. I mostly teach courses on artificial intelligence. This landing page is intended to serve both as a collection of my courses, and a list of materials that I myself am learning from. In addition to this ever lengthening list, I also maintain a modest library of books related to the topic, and encourage students to browse the library and come borrow books from me if they're interested.

Explore core AI algorithms through interactive visualizations. These tools are designed to help you understand how fundamental algorithms work by watching them execute step-by-step.

A* Pathfinding

Visualize the A* search algorithm finding optimal paths through grids. Compare heuristics like Manhattan, Euclidean, and Dijkstra.

Search Heuristics

Minimax & Alpha-Beta

Watch game trees expand with minimax and alpha-beta pruning. Play Tic-Tac-Toe and Nim against optimal strategies.

Game Theory Adversarial

K-Nearest Neighbors

Explore the KNN algorithm with interactive classification. Add training points, adjust K, and visualize decision boundaries.

Classification ML

Decision Tree

Build and visualize decision trees for classification. Watch how splits partition the feature space and explore tree structure.

Classification ML

Embeddings

Project MNIST and CIFAR-10 embeddings into 2D using PCA, t-SNE, and UMAP. Watch the embeddings evolve during training.

ML Visualization

Constraint Satisfaction

Solve Shidoku (4x4 Sudoku) with backtracking, AC-3, MRV, and MAC. Visualize search trees and arc consistency.

CSP Backtracking

Bayesian Networks

Explore probabilistic graphical models. Edit conditional probability tables and compute posterior probabilities given evidence.

Probabilistic Inference

Expert Systems

Learn forward and backward chaining inference. Watch rules fire step-by-step and see how conclusions are derived from facts.

Inference Knowledge

Perceptron Learning

Watch a perceptron learn a linear decision boundary step-by-step. Explore convergence and failure on non-separable data.

Classification Linear Models

CSE 10124 · Building ChatGPT

Changelog

Updating lab and hw templates Feb 04, 2026
Updating lab template Feb 04, 2026
Trying to force build? Jan 29, 2026
Trying to fix teaser height Jan 29, 2026

CSE 30124 · Introduction to Artificial Intelligence

Changelog

Trying to rebuild site, missing readings Feb 02, 2026
Merge pull request #2 from wtheisen/claude/dtree-visualizer-Ws0Or Jan 30, 2026
Add Design Mode to Decision Tree Visualizer Jan 30, 2026
Add Decision Tree Visualizer Jan 30, 2026

This is a list of the things I watch, read, or listen to that I find interesting!

i found the model weights for opus 4.5 and i have to say it looks very impressive https://t.co/rQTtnTYBxV

@djcows

i found the model weights for opus 4.5 and i have to say it looks very impressive https://t.co/rQTtnTYBxV

Tweet Feb 2, 2026

The future of intelligence

Demis Hassabis, Hannah Fry

Video Feb 2, 2026

Is human data enough?

David Silver, Hannah Fry

Video Feb 2, 2026

A billion years of evolution in a single afternoon

George Church, Dwarkesh Patel

Video Feb 2, 2026

The past few days on X https://t.co/fBbN23tXlj https://t.co/iEpVj9f6VL

@Discord_Lies

The past few days on X https://t.co/fBbN23tXlj https://t.co/iEpVj9f6VL

Tweet Feb 1, 2026

How DeepSeek Rewrote the Transformer

Welchlabs

Video Feb 1, 2026

Can humans make AI any better?

Welchlabs

Video Feb 1, 2026

a bunch of dumb guys asked their local LLMs to output text simulating Reddit and are freaking out about machine intellig…

@cannibality

a bunch of dumb guys asked their local LLMs to output text simulating Reddit and are freaking out about machine intelligence because their statistically probable text generator is generating statistically probable text. https://t.co/yNMwGysOv9

Tweet Jan 31, 2026