3  Prerequisite Map

This book is designed to point back into Wayward House Mathematics rather than silently assuming missing knowledge.

3.1 Core prerequisite path

Needed here Best place to point back
Functions and composition vol-03/04-functions-relations.qmd
Exponents and logs vol-04/01-exponents-logarithms.qmd
Sequences and accumulation vol-04/04-sequences-series.qmd
Differential calculus and gradients vol-05/02-differential-calculus.qmd
Integral/calculus intuition vol-05/03-integral-calculus.qmd
Probability and distributions vol-06/01-probability-theory.qmd, vol-06/02-distributions.qmd
Inference and regression vol-06/03-statistical-inference.qmd, vol-06/04-data-analysis.qmd
Matrices and eigenvalues vol-07/linear-algebra/01-matrices-systems.qmd, vol-07/linear-algebra/02-eigenvalues.qmd
Numerical methods vol-07/numerics/01-numerical-methods.qmd
Optimisation vol-07/optimisation/01-linear-programming.qmd, vol-08/07-nonlinear-optimisation.qmd
Signals and sampled systems vol-08/02-discrete-systems-signals.qmd
Estimation and filtering vol-08/05-estimation-inverse.qmd

3.2 What the current series already covers well

  • enough algebra and calculus for gradient-based learning
  • enough linear algebra for PCA, embeddings, and matrix factorisation
  • enough probability and inference for supervised learning foundations
  • enough optimisation and numerics for training and fitting logic
  • enough signals and estimation for time-series and state-space ML

3.3 What this follow-on book must add explicitly

  • loss functions as modelling choices
  • classification and probabilistic prediction
  • regularisation and generalisation
  • information theory
  • matrix calculus in ML form
  • backpropagation and layered composition
  • representation learning and modern AI objectives