ML Math / Algo · 2026-03-10

Algorithms for Decision Making — Study Skeleton

A lightweight topic map for future deep dives in ML math and algorithms.

Reference source for study planning:

https://www.peregianhub.com.au/chatmedia/aichat/00000277-Alogorithms%20for%20Decision%20Making.pdf

This is a skeleton only for topics and sequencing.

Topic map

Probability foundations
Utility and decision theory
Dynamic programming
Markov decision processes
Planning under uncertainty
Approximate inference and optimization

Suggested progression

flowchart LR
  A[Probability] --> B[Decision Theory]
  B --> C[Dynamic Programming]
  C --> D[MDPs]
  D --> E[Planning Under Uncertainty]
  E --> F[Approximate Methods]

One possible study sequence

Core equation placeholders

Bellman-style recursion template:

V^*(s)=\max_a\left[R(s,a)+\gamma\sum_{s'}P(s'\mid s,a)V^*(s')\right]

Bayesian update template:

P(\theta\mid x)=\frac{P(x\mid\theta)P(\theta)}{P(x)}

Next iterations

Fill one worked example per topic.
Add one coding notebook or script per section.
Connect each concept to one real decision problem.