AI, taught step by step

This module builds the mental model underneath everything else in the curriculum. We start with explicit rules, then add uncertainty, then explore search, so students can see AI as a chain of concrete decisions rather than a pile of mysterious buzzwords.

Question we are chasing: How can a machine move from rigid step-by-step instructions to making sensible choices in a messy, uncertain world?

This module is where the course shifts from explicit rules to learned patterns. Instead of telling the machine exactly what to do in every case, we give it examples, define success, and let it infer a decision rule from the data.

Question we are chasing: How can a machine study examples, extract useful patterns, and make predictions on cases it has never seen before?

Theory is only half the story. In this module we roll up our sleeves and learn the craft behind every successful ML project: preparing data, engineering features, and strengthening our statistical intuition. These are the skills that separate a notebook experiment from a model you can actually trust.

Question we are chasing: What does raw, messy, real-world data need before a model can learn anything useful from it?

Building a model is one thing; training it well and knowing whether it actually works is another. This module covers the engine room of ML: how optimization drives learning, how bias and variance shape model behavior, how to pick the right scoreboard, and how to run experiments you can trust.

Question we are chasing: How do we train a model effectively, measure its true performance, and make sure our results are not just a fluke?

A model that works is not enough—it also has to be understandable, fair, and ready for the real world. This module covers the human side of AI: explaining predictions, confronting bias, and deploying models responsibly.

Question we are chasing: How do we make sure an AI system is not just accurate but also transparent, fair, and safe to deploy?

This module introduces the core architecture behind much of modern AI. Students follow information as it moves through layers, is transformed by weights and activations, and eventually becomes a prediction that can be improved through feedback.

Question we are chasing: How do large collections of simple numerical operations combine into a model that can recognize patterns humans struggle to hand-code?

Some data points only make sense when you know what came before them. This module studies models built for ordered information such as language, audio, weather, and time series, where sequence and memory matter as much as the current input.

Question we are chasing: How can a model represent the past well enough to make a strong decision about what is happening now or what should happen next?

This module explains the modern language-model stack from the inside out. Students see how words become vectors, how attention lets models choose context dynamically, and how large-scale next-token training turns those ingredients into systems that can write, summarize, and answer questions.

Question we are chasing: How can a machine represent meaning, decide which context matters, and then generate fluent language one token at a time?

This module looks beyond the standard supervised-learning workflow. Students explore systems that learn from delayed rewards and systems that train collaboratively while keeping raw data distributed, which introduces the real-world constraints of strategy, privacy, and deployment.

Question we are chasing: How can AI systems keep improving in realistic environments where feedback is delayed, data is sensitive, and decisions have long-term consequences?