Neuroscience

A working mental model of the cosmos — how we actually know what we know (light, telescopes, spectra), what the solar system, stars, and galaxies are, how the universe began, and how the search for other worlds and life is going. The trunk gives you the full map; clusters dive into the naked-eye sky, observatories, the sun, the planets, black holes, cosmology, and alien worlds.

A comprehensive, 3-pillar curriculum that builds durable understanding of artificial intelligence and machine learning — from the mathematical foundations and classical algorithms, through modern deep learning architectures, to the societal implications of deploying AI in the real world. Emphasizes timeless principles over transient tools so knowledge remains valuable as the field evolves.

A practical, 4-pillar skill track that takes you from zero to shipping. You will learn to think in code, master one language deeply (Python), turn scripts into real software with tests and version control, and put your work in front of users with a deploy, a database, and modern AI-assisted workflows. Deliberately narrower than a role-track CS degree: if the question is 'can I build and ship software,' this is the answer.

A rigorous curriculum on the Earth as a coupled system — its biogeochemical cycles, ecosystems, climate, pollution pathways, and the sustainability and policy frontier. Built for working professionals who already had a weak intro environmental class and want a complete, mechanistic mental model: how the carbon cycle actually works, why climate models project what they do, where pollutants travel, and which interventions move planetary boundaries. Every course pairs scientific mechanism with a real-world implication so the learner can reason about environmental decisions, not just recite definitions.

How machine learning actually works — from learning-as-optimization through the classical toolkit to deep learning and foundation models. The depth step after AI Fundamentals: real mechanics, no code or graduate math required.

A working understanding of discrete mathematics — the math of countable, structural objects: sets, logic, counting, graphs, integers, algorithms. The trunk gives you the toolkit; clusters dive into combinatorics, graph algorithms, number theory, logic and computation, cryptography, and information theory.