Part III — The Probability Core

Booklet: Abridged Math Foundations for Signal Processing, Machine Learning, and Artificial Intelligence, Lessons 14–17 (PDF).

Probability models; counting — permutations, combinations, partitions; conditioning and Bayes’ rule; discrete and continuous random variables; functions of a random variable and the expected value rule; expectation and variance; joint distributions, functions of several random variables, and linearity of expectation; conditioning; the normal distribution.

21 exercises across Lessons 14–17 — 10 [Hand], 11 [Proof].

Exercises

Worked sets are linked below as they are completed.

• Lesson 14 — Probability Models, Counting, Conditioning, and Bayes’ Rule — 9 exercises (4 [Hand], 5 [Proof]) — ✓ done
• Lesson 15 — Discrete Random Variables and Expectation — 4 exercises (2 [Hand], 2 [Proof]) — ✓ done
• Lesson 16 — Multiple Random Variables and Conditioning — 4 exercises (2 [Hand], 2 [Proof])
• Lesson 17 — Continuous Random Variables and the Normal — 4 exercises (2 [Hand], 2 [Proof])