Iliad
Intensives →

Iliad Intensive Curriculum

The Iliad Intensive is a month-long, full-time AI alignment course for students with strong mathematics, physics, or theoretical-CS backgrounds. The materials are self-contained lecture notes and worksheets on various topics, and pointers for further study. About 20 contributors developed them. We welcome feedback via issues on GitHub.

A — Alignment

B — Learning

    • B.3Singular Learning Theory

      Singular learning theory (SLT) places degeneracy as a core part of understanding how neural networks learn. We cover the parameter-function map, the meaning of degeneracy through the local learning coefficient, to Watanabe's free energy formula and Bayesian phase transitions.

    • B.4Training Dynamics

      Exact learning dynamics of deep linear networks -- loss-landscape geometry, balanced gradient flow and the NTK, the rich (saddle-to-saddle) and lazy regimes, their mixed unification, and the implicit bias of SGD noise.

C — Abstractions, Representations, and Interpretability

D — Agency

  • D.1Decision Theory and Reinforcement Learning

    • D.1.1Preferences to Rewards

      Building from preferences and a minimal set of axioms to a utility function expressible as a sum of discounted rewards: the familiar framing in reinforcement learning.

    • D.1.2Reinforcement Learning

      The Bellman equations and what follows from them: the existence of optimal policies, the policy improvement theorem, the rate of convergence of Bellman updates, and the convergence of Q-learning.

  • D.3AIXI

    • D.3.1Solomonoff Induction

      How an idealized agent should predict. A Bayesian mixture over a countable class of computable hypotheses learns to predict any sequence, with total error bounded by the description length of the truth -- a formal Occam's razor.

    • D.3.2AIXI

      Exploring the Bayesian optimal policy for history based reinforcement learning.

    • D.6Instrumental Convergence

      We discuss a simple mathematical formalization of what it means to "seek power" in a Markov decision process (MDP), and conditions under which such behavior emerges.