Syllabus


Lectures Sections in Text Brief Description
3/27 1.1 Systems of Linear Equations
3/29 1.2 Row Reduction and Echelon Forms
3/31 1.3, 1.4 Vector Equations; Matrix Equations
4/3 1.4, 1.5 Matrix Equations; Solutions Sets of Linear Equations
4/5 1.7 Linear Independence
4/7 4.1, 4.2 Vector spaces, linear transformation, null space, column space
4/10 4.2, 1.9 Reading injectivity and surjectivity of $T: \mathbb R^n \to \mathbb R^m$ from the representing matrix.
4/12 1.9, 2.1 Matrix Operations
4/14 2.2 Inverse of a Matrix
4/17 2.3 Invertible Matrix Theorem
4/19 4.3 Linear independent sets; bases
4/20 Midterm Exam I 4:30-6:30pm
4/21 2.9 Dimension and rank
4/24 4.4/4.7/5.4 (variant) Coordinates, matrix of a transformation, change of basis
4/26 4.7, 3.1 Determinants and Properties
4/28 3.2 Properties of Determinants
5/1 5.1, 5.2 Eigenvalues and Characteritic Equation
5/3 5.2, 5.3 Characteristic Equation, Diagonalization
5/5 5.3, 5.4 Diagonalization and linear transformations
5/8 4.9,5.8 (optional) Intro to Markov Chains, Iteration Method for Eigenvalues
5/10 6.1-6.2 Orthogonality
5/11 Midterm Exam II 4:30-6:30pm
5/12 6.3 Projections
5/15 6.4 Gram-Schmidt Process
5/17 7.1 Diagonalization of Symmetric Matrices
5/19 7.4 Singular Value Decomposition
5/22 7.4 Singular Value Decomposition
5/24 7.4, 7.5 Other applications, e.g., Principal Component Analysis,
SVD and image processing, Facial Recognition, etc.
5/26 Wrap it up  
5/29 Memorial Day Holiday No classes
6/1 Final Exam 11:30 am - 2:30 pm


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Last updated November 15, 2017