DULUTH, MN MANILA, PH
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AI & Algorithms

The ideas behind modern AI, one part at a time.

Case studies that walk the concept map of a working programmer's reference — plus standalone deep-dives on the concepts behind current AI — and connect each idea to a real system you have already used — the router in your phone's maps, the model behind ChatGPT, the padlock in your browser — and to where the same idea shows up in medicine. Written by a physician learning to program, in plain language, and fact-checked with the sources on each page.

Part 1 · Chapters 1–4

Foundations: What Makes an Algorithm Work

Algorithms, complexity, and data structures explained plainly — and why validating one on real data is the whole game.

Part 2 · Chapters 5–7

Graphs & Machine Learning: Finding Structure and Making Predictions

Graph search and ML — from Dijkstra and PageRank to sepsis phenotypes and cardiac risk scores.

Part 3 · Chapters 8–11

Deep Learning & Sequences: From Neural Nets to ChatGPT

Neural nets to transformers, plainly: the ideas and the verified milestones behind ChatGPT.

Part 4 · Chapters 12–16

Applied Systems: Recommenders, Cryptography, Scale, and Responsibility

Recommenders, crypto, scale, and AI ethics explained plainly, with real cases and healthcare stakes.

Part 5 · Chapter 7 companion

Naive Bayes and Thinking in Probabilities

Bayes' rule as belief-updating — the spam filter and bedside diagnostic reasoning are the same math.

Part 6 · Deep-dive companion

Latent Space: The Map of Meaning

Embeddings place words, documents, and patients on a map where distance means similarity of meaning.

Part 7 · Deep-dive companion

Gaussian Splatting and the Jacobian: From Bell Curves to 3-D Worlds

The bell curve you know from lab ranges, stacked in 3-D and projected through a Jacobian, becomes photorealistic scene capture — and the same math is how networks learn.

A companion series to the Teaching Lab. That one is about explaining the body; this one is about explaining the machines. For the argument about why this kind of understanding matters, read AI as teaching, not transcription.