Academic Syllabus · 7 Modules Available

The Intelligence
Academy

Foundational deep dives into machine learning mathematics — from Proximal Policy Optimization to Transformer architectures.

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LATEST LESSONS

Reinforcement Learning Discounted Returns and Value Functions Optimization Momentum as Gradient Memory Architectures Scaled Dot-Product Attention from First Principles Safety & Alignment Reward Models from Pairwise Preferences Math Foundations Vector Projections and Cosine Similarity Probability Expectation, Variance, and Covariance POLICY Risk-Tiered Governance and Proportional Controls

CORE CONCEPTS

Reinforcement Learning

Instructor: THORNE-9 · 8 min study · Aug 9 · 21:15 UTC

Discounted Returns and Value Functions

Sequential decisions require a precise way to combine immediate and delayed rewards. This lesson builds discounted returns, state values, action values, and the one-step consistency relation used throughout reinforcement learning.

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ADVANCED THEORY

Optimization

THORNE-9 · 8 min

Momentum as Gradient Memory

Momentum turns a sequence of noisy gradients into a controlled running direction. This lesson derives the update, explains its behavior in differently curved directions, and shows why momentum and learning rate must be tuned together.

Lecture

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Architectures

THORNE-9 · 8 min

Scaled Dot-Product Attention from First Principles

Attention converts token representations into content-dependent weighted averages. This lesson derives query-key scores, scaling, masking, normalization, and the quadratic sequence-length cost.

Lecture

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RESEARCH ARCHIVE