Introduction
Module 1: Foundations
1.
Overview
2.
Probability, distributions, and expectation
3.
Conditional probability and Bayes' rule
4.
Sampling and Monte Carlo estimation
5.
Entropy, cross-entropy, and KL divergence
6.
Vectors and dot products
7.
Matrices and matrix-vector multiplication
8.
Derivatives, gradients, and the chain rule
9.
Project: Monte Carlo conjunction probability
Light
Rust
Coal
Navy
Ayu
ML for Spacepower Simulations
Introduction