Math 24
Linear Algebra

Last updated May 25, 2016 16:18:35 EDT

General Information Syllabus HW Assignments Course Resources


Syllabus

The following is a tentative syllabus for the course. This page will be updated irregularly.
On the other hand, the weekly syllabus contained in the Homework Assignments page will always be accurate.


Lectures Sections in Text Brief Description
3/28 1.1, 1.2 Introduction and Vector Spaces
3/30 1.2, 1.3 Vector Spaces and Subspaces
3/31 (x-hour)   Proofs and Induction
4/1 1.3, 1.4 Subspaces, Linear combinations
4/4 1.4, 1.5 Systems of Linear Equations, Linear Independence and Dependence
4/6 1.6 Bases and Dimension
4/7 (x-hour)    
4/8 1.6 Bases and Dimension
4/11 2.1 Linear Transformations, Nullspace and Range
4/13 2.1 Linear Transformations, Nullspace and Range
4/14 (x-hour)    
4/15 2.2 Matrix Representations of a linear transformation
4/18 2.3 Matrix Representations of Compositions of linear maps: matrix multiplication
4/20 Midterm Exam I All material through 2.2
4/21 (x-hour) 2.4 Isomorphism and Invertibility
4/22 2.5 Change of Basis
4/25 3.1,3.2 Elementary Matrix Operations and Matrices
4/27 3.2,3.3 Rank of a Matrix and Inverses
4/28 (x-hour) None  
4/29 3.3,3.4 Systems of Linear Equations
5/2 4.2-4.4 An overview of determinants
5/4 5.1 Eigenvalues and Eigenvectors
5/5 (x-hour) None  
5/6 5.2 Diagonalizability
5/9 5.2 More on diagonalizability
5/11 Midterm Exam II Material through 5.2 (no differential equations)
5/12 (x-hour) 2.7, 5.2 Applications to differential equations
5/13 5.3 Matrix Limits and Markov Chains
5/16 6.1 Inner Product Spaces and Norms
5/18 6.2 Gram-Schmidt Orthogonalization
5/19 (x-hour) None  
5/20 6.3 The Adjoint of a Linear Transformation
5/23 6.3 Linear Regression; minimal solutions
5/25 6.5 Unitary/Orthogonal Diagonalization
5/26 (x-hour) None  
5/27 6.5 Unitary/Orthogonal Diagonalization; SVD and the four subspaces
5/30 Memorial Day Holiday No classes
6/2 Final Exam 8-11am


T. R. Shemanske
Last updated May 25, 2016 16:18:35 EDT