This is a very thorough and well designed visual introduction to Gaussian Processes and Bayesian optimisation. The article features interactive visualisations, which I found great to truly get a feel for what's happening.

A fantastic collection of free textbooks on algorithms for optimization, decision making and validation.

An interesting analysis of various optimization algorithms applied to a simple dynamical programming problem. Features beautiful visualizations of those algorithms.

Fantastic introduction to the simulated annealing metaheuristic in Python. This is a powerful method to build good approximate solutions to optimization problems.

Detailed use cases of the OR-Tools library for optimization problems. Many problems can be solved in a more efficient way with linear programming, and this library makes it a breeze to do so.

This a collection of all the optimization metaheuristic you can possibly imagine, with comments on how to implement them and what parameters can be tuned. The aim is to take it one algorithm per afternoon.