Machine Learning

A guided tour through philosophical thinking — how to reason carefully, evaluate arguments, navigate ethics, and engage with the great questions about knowledge, reality, mind, and society. The trunk gives you orientation across the whole field; clusters dive deep on logic, ethics, epistemology, metaphysics, mind, politics, and the history of ideas.

A working understanding of linear algebra — vectors, matrices, eigenvalues, and the decompositions that power modern data, graphics, and AI. The trunk gives you the geometric and algebraic core; clusters dive into vector spaces, systems and rank, eigentheory, orthogonality and projections, applications, and numerical computation.

A non-technical curriculum that builds real understanding of AI — how it works, why it fails, and how to use it intelligently. No math, no code, just durable mental models that make you a confident, critical user of AI tools.

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.

Build the universal mental model that every database is implemented against — the relational model, schemas and keys, indexes, transactions, concurrency, PostgreSQL architecture, production operations, modeling patterns, and the modern data ecosystem (NoSQL, distributed SQL, warehouses, vectors, streaming).

A working understanding of the craft of selling — buyer psychology, discovery, qualification, objection handling, negotiation, and pipeline math. The trunk walks one complete deal motion from prospect to close to expansion; clusters dive into outbound mechanics, discovery frameworks, objection playbooks, negotiation, founder-led sales, and enterprise deals.