Linear Algebra (2026)

6 videos

This is a standard university-level linear algebra course.

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Resources

Syllabus

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Course syllabus — grading, policies, and the full schedule

A one-year course · Lay, Lay & McDonald, Linear Algebra and Its Applications, 5th ed. · Chapters 1–8

Email: dt323259@gmail.com  ·  Phone: 435-619-1784  ·  Course site: timpson-lyceum.vercel.app

Course Description

An introduction to linear algebra, from the concrete methods of solving linear systems to the abstract structures that unify them. Topics: systems of linear equations and row reduction; matrix algebra and invertibility; determinants; vector spaces, bases, dimension, and rank; eigenvalues, eigenvectors, and diagonalization; orthogonality, projections, and least squares; symmetric matrices, quadratic forms, and the singular value decomposition; and the geometry of vector spaces. The course balances computational fluency with conceptual understanding and short proofs, and draws applications throughout — from computer graphics and Markov chains to regression and image compression.

Text: Lay, Lay & McDonald, Linear Algebra and Its Applications, 5th edition. The course covers Chapters 1–8 in full.

Learning Objectives

By the end of the year, you will be able to:

  • Solve and analyze linear systems by row reduction, and interpret solution sets geometrically.
  • Perform matrix algebra fluently, determine invertibility, and apply the Invertible Matrix Theorem.
  • Compute determinants and use their properties.
  • Reason about vector spaces, subspaces, linear independence, bases, dimension, rank, and coordinates.
  • Find eigenvalues and eigenvectors, diagonalize matrices, and apply them to dynamical systems.
  • Use inner products, orthogonal projections, the Gram–Schmidt process, and least-squares methods.
  • Diagonalize symmetric matrices, analyze quadratic forms, and compute the singular value decomposition.
  • Read and write short proofs, and justify your reasoning precisely.

Materials

  • Textbook: Lay, Lay & McDonald, Linear Algebra and Its Applications, 5th ed.
  • Calculator: a scientific or graphing calculator.
  • Course site: your Timpson Lyceum account at timpson-lyceum.vercel.app — lectures, weekly problem sets, posted solutions, and attendance all live here.

Grading

Your grade is submitted each semester. The same weighting applies within each semester, with that term's cumulative exam (midterm in the fall, final in the spring) as its big exam.

CategoryWeight
Attendance10%
Homework (weekly problem sets)20%
Quizzes (weekly)10%
Unit Tests (four)30%
Midterm & Final30%
Base total100%
Extra Creditup to +5%

Maximum possible grade: 105%.

Extra credit. Every problem set, every unit test, and both exams include an optional extra-credit problem. Do them and they lift your final grade — together they can add up to 5 percentage points (a 105% ceiling). There is no extra credit on quizzes or attendance.

Attendance — 10%. Honor system. Attend the weekly session, or — if you must miss it — watch the recorded lecture on the course site and mark it watched. Credit is per lecture. One 3-hour block is a full week of the course, so keep up.

Homework — 20%. A problem set for each section, due weekly. Solutions are posted the moment the set is due, so you get immediate feedback — and no late work is accepted. Each set carries an optional extra-credit problem.

Quizzes — 10%. A short quiz at the start of most classes on the previous week's material — quick retrieval practice to keep ideas fresh across the week between blocks.

Unit Tests — 30% (four across the year, ~15% each per semester). Each test has two parts: a unit portion on the newest material and a cumulative portion reaching back over everything so far — plus an optional extra-credit problem.

Midterm & Final — 30%. The midterm (end of fall) covers Chapters 1–4; the final (end of spring) covers Chapters 5–8 and leans cumulative. Each has a unit portion, a cumulative portion, and an extra-credit problem. Score replacement: your midterm replaces your lower Test 1 / Test 2 score when it is higher, and your final replaces your lower Test 3 / Test 4 score — so a strong exam can rescue one weak unit test.

Grade Scale

A 93–100 · A− 90–92 · B+ 87–89 · B 83–86 · B− 80–82 · C+ 77–79 · C 73–76 · C− 70–72 · D+ 67–69 · D 63–66 · D− 60–62 · F below 60

Extra credit can carry a final grade above 100%, to a maximum of 105%.

Policies

Late work. None accepted. Because solutions post exactly when an assignment is due, turning something in late isn't possible — the trade is that you always get fast feedback.

Missed tests & exams. There are no make-ups for a missed unit test, midterm, or final. A missed unit test is cushioned by the score-replacement rule above: your midterm or final can stand in for your lowest unit test in that semester.

Course Schedule

Twenty-seven weeks of instruction on a 3-hour weekly block, covering all of Chapters 1–8. Two flexible catch-up / review weeks in the spring absorb schedule disruptions; dates are approximate and may shift.

Semester I — Chapters 1–4 (Weeks 1–14)

WkTopicsAssessment
11.1 Systems of Linear Equations · 1.2 Row Reduction & Echelon FormsCourse intro
21.3 Vector Equations · 1.4 The Matrix Equation Ax = b · 1.5 Solution Sets
31.6 Applications of Linear Systems · 1.7 Linear Independence
41.8 Introduction to Linear Transformations · 1.9 The Matrix of a Linear Transformation · 1.10 Linear Models
52.1 Matrix OperationsTest 1 · Ch 1
62.2 The Inverse of a Matrix · 2.3 Characterizations of Invertible Matrices
72.4 Partitioned Matrices · 2.5 Matrix Factorizations (LU) · 2.6 Leontief Input–Output
82.7 Computer Graphics · 2.8 Subspaces of ℝⁿ · 2.9 Dimension and Rank
93.1 Introduction to Determinants · 3.2 Properties of Determinants · 3.3 Cramer's Rule, Volume & Transformations
104.1 Vector Spaces and SubspacesTest 2 · Ch 2–3
114.2 Null Spaces, Column Spaces & Linear Transformations · 4.3 Linearly Independent Sets; Bases
124.4 Coordinate Systems · 4.5 The Dimension of a Vector Space · 4.6 Rank · 4.7 Change of Basis
134.8 Applications to Difference Equations · 4.9 Applications to Markov ChainsMidterm review
14Midterm Examination — Chapters 1–4Midterm

Semester II — Chapters 5–8 (Weeks 15–29)

WkTopicsAssessment
155.1 Eigenvectors and Eigenvalues · 5.2 The Characteristic Equation
165.3 Diagonalization · 5.4 Eigenvectors and Linear Transformations
175.5 Complex Eigenvalues · 5.6 Discrete Dynamical Systems · 5.7 Differential Equations · 5.8 Iterative Estimates
186.1 Inner Product, Length, and OrthogonalityTest 3 · Ch 5
196.2 Orthogonal Sets · 6.3 Orthogonal Projections
206.4 The Gram–Schmidt Process · 6.5 Least-Squares Problems
216.6 Applications to Linear Models · 6.7 Inner Product Spaces · 6.8 Applications of Inner Product Spaces
227.1 Diagonalization of Symmetric MatricesTest 4 · Ch 6
237.2 Quadratic Forms · 7.3 Constrained Optimization
247.4 The Singular Value Decomposition · 7.5 Image Processing & Statistics
25Catch-up & review
268.1 Affine Combinations · 8.2 Affine Independence · 8.3 Convex Combinations
278.4 Hyperplanes · 8.5 Polytopes · 8.6 Curves and Surfaces
28Catch-up & final review
29Final Examination — Chapters 5–8Final
  1. 1
    Introduction to Linear Algebra– Lecture 0 (Linear Algebra)

    Introduction to Linear Algebra– Lecture 0 (Linear Algebra)

    29:54

  2. 2
    Systems of Linear Equations and Echelon Forms – Lecture 1 (Linear Algebra)

    Systems of Linear Equations and Echelon Forms – Lecture 1 (Linear Algebra)

    1:35:56

  3. 3
    Vector Equations and the Matrix Equation Ax = b – Lecture 2 (Linear Algebra)

    Vector Equations and the Matrix Equation Ax = b – Lecture 2 (Linear Algebra)

    1:27:36

  4. 4
    Solution & Applications of Linear Systems and Linear Independence – Lecture 3 (Linear Algebra)

    Solution & Applications of Linear Systems and Linear Independence – Lecture 3 (Linear Algebra)

    1:24:01

  5. 5
    Linear Transformations, Matrices, and Linear Models – Lecture 4 (Linear Algebra)

    Linear Transformations, Matrices, and Linear Models – Lecture 4 (Linear Algebra)

    1:47:57

  6. 6
    Matrix Operations and the Inverse of a Matrix – Lecture 5 (Linear Algebra)

    Matrix Operations and the Inverse of a Matrix – Lecture 5 (Linear Algebra)

    1:57:32

Problem Sets

Builds on