These are the recommended lecture readings to accompany the course. All readings are taken from Eric Lehman, Tom Leighton, and Albert Meyer, Mathematics for Computer Science (PDF). The textbook is available under a CC BY-SA license.
Unit 1: Proofs
Lecture 01 - Predicates, Sets, and Proofs: Sections 1.0–1.7, 3.1–3.3, 3.6
Lecture 02 - Contradiction and Induction: Sections 1.8–1.9, 4.1, and 5.1
Lecture 03 - Casework and Strong Induction: Sections 5.2–5.3
Lecture 04 - State Machines: Updated State Machines Chapter
Unit 2: Algorithm Analysis
Lecture 05 - Sums: Sections 14.1–14.6
Lecture 06 - Asymptotics: Section 14.7
Lecture 07 - Recurrences: Sections 22.1–22.5 (skip 22.4)
Unit 3: Number Theory
Lecture 08 - Divisibility: Sections 9.1–9.2
Lecture 09 - Modular Arithmetic: Sections 9.6–9.7, 9.9
Lecture 10 - Cryptography: Sections 9.5, 9.8, 9.11
Unit 4: Graphs
Lecture 11 - Graphs and Coloring: Sections 12.1–12.3, 12.6
Lecture 12 - Matching: Sections 12.5–12.5.1, and the Updated State Machines chapter, section 4
Lecture 13 - Connectivity and Trees: 12.7–12.9, 12.11
Lecture 14 - Digraphs and DAGs: 10–10.2, 10.5
Unit 5: Counting
Lecture 15 - Relations and Counting: 4.3–4.4; 10.4, 10.6, 10.8, 10.10, 10.11
Lecture 16 - Counting Techniques: 4.5, 15.1–15.4, 15.8
Lecture 17 - More Counting Techniques: 15.5–15.7, 15.9–15.10
Unit 6: Probability
Lecture 18 - Probability: Section 17 (can skip 17.4)
Lecture 19 - Conditional Probability: Sections 18.0–18.6
Lecture 20 - Independence: Sections 18.7–18.9, 17.4
Lecture 21 - Random Variables: Sections 19–19.3
Lecture 22 - Expectation: Sections 19.4–19.6
Lecture 23 - Expectation and Variance: Chapter 20 (reading shared with lecture 24)
Lecture 24 - Large Deviations: Chebyshev and Chernoff Bound, Wrap Up: Chapter 20 (reading shared with lecture 23)
