The City College of New YorkCCNY
Department of Mathematics
Division of Science

Math 346 Videos, Summaries, Problem Sets

1.1 Introduction to Linear Systems and 1.2 Matrices, Vectors, and Gauss-Jordan Elimination

Recitation: Elimination with Matrices

Strang: Elimination with Matrices (only watch first 19 minutes)

1.3 On the Solutions of Linear Systems; Matrix Algebra

3Blue1Brown: Vectors, what even are they?

Recitation: Geometry of Linear Algebra

Strang: The Geometry of Linear Equations

Problems and Solutions

Solving Ax = 0 (recitation video)

Solving Ax = b (recitation video)

2.1 Introduction to Linear Transformations and Their Inverses and 2.2 Linear Transformations in Geometry

3Blue1Brown: Linear Transformations

2.2 Linear Transformation in Geometry

3D Linear Transformation (3blue1brown)

2.3 Matrix Products

3Blue1Brown: Matrix Products

2.4 Inverse Matrices

Inverse matrices, (skip) column space = image, and (skip) nullspace = kernel (3blue1brown)

Recitation: Inverse Matrices

Strang: Inverse Matrices

Problems and Solutions

Matrix Mult. and Inverse Matrix (lecture Beyer)

3.1 Image and Kernel

Inverse matrices, column space = image, and nullspace = kernel (3blue1brown)

Nonsquare matrices (3blue1brown)

Overview of Key Ideas (recitation video)

3.2 Subspaces

Linear combination, span, basis vectors (3blue1brown)

Vector Subspaces (recitation video)

Vector Subspaces (recitation video 2)

Column Space and Nullspace (lecture Strange)

Solving Ax = 0: Pivot Variables, Special Solutions (lecture Strange)

3.3 The Dimension of a Subspace

Inverse Matrix, Rank, and Nullity (3blue1brown)

Independence, Basis, and Dimension (lecture Strange)

Basis and Dimension (recitation video)

The Four Fundamental Subspaces (lecture Strange)

The Four Fundamental Subspaces (recitation)

General Solution and Particular Solution (Beyer Lecture)

3.4 Coordinates and Change of Basis

Change of Basis (3blue1brown)

4.1 Introduction to Linear Spaces

Abstract Vector Spaces (3Blue1Brown)

Beyer Lecture

Recitation video

5.1 Orthogonal Projections and Orthogonal Bases

Orthogonal Spaces (recitation video)

5.2 Gram Schmidt

Gram Schmidt (Gil Strang lecture)

Gram Schmidt (recitation video)

QR factorization (recitation video)

5.4 Least Squares and Data Fitting

Projection onto Subspaces (recitation video)

Least Squares (recitation video)

Chapter 6 Determinants

Determinants (3blue1brown)

Determinant Formulas and Cofactors (lecture Strang)

Cramer's Rule, Inverse Matrix, and Volume (lecture Strang)

Chapter 7 Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors (3blue1brown)

Eigenvalues and Eigenvectors (lecture Strang)

Eigenvalules and Eigenvectors (recitation video)

A Quick Trick to Compute Eigenvalues (3blue1brown)

Chapter 8

Singular Value Decomposition

Singular Value Decomposition (recitation video)

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