Machine Learning

Learning objectives Use spaced practice and active recall to retain concepts. Combine reading with coding and small projects. Avoid common traps (passive watching, skipping exercises). Spaced practice Do not cram. Spread your study over time: e.g. 30–60 minutes per day on the curriculum, rather than one long session per week. Revisit earlier chapters when you hit later material (e.g. when you do TD, recall what you did in Monte Carlo). Spacing strengthens long-term retention. ...

Learning objectives See the recommended order of topics before (or alongside) RL: math, programming, optional supervised learning. Know what this curriculum assumes and where to fill gaps. Prerequisite roadmap (overview) Pt 1 — Foundations Programming: Variables, types, conditionals, loops, functions, basic data structures (lists, dicts). Language: Python. If you have no programming, start with the Learning path Phase 0 and Prerequisites: Python. Probability and statistics: Sample mean, variance, expectation, law of large numbers. Used in bandits, Monte Carlo, and value functions. See Math for RL: Probability. Linear algebra: Vectors, dot product, matrices, matrix-vector product. Used in value approximation \(V(s) = w^T \phi(s)\) and gradients. See Math for RL: Linear algebra. Calculus: Derivatives, chain rule, partial derivatives. Used in policy gradients and loss minimization. See Math for RL: Calculus. NumPy (and optionally Pandas, Matplotlib): Arrays, indexing, random numbers, plotting. See Prerequisites: NumPy, Matplotlib, Pandas. Pt 2 — Toward deep RL ...