Matrices and Matrix Calculations (M340L)

Course information:

Unique: 54165

Lecture: 3:30-5:00pm, T Th, CPE 2.214

There is no required text for this course. Learning modules covering each lecture can be found on Quest at https://quest.cns.utexas.edu/

Recommended course texts:

1. Linear Algebra and Its Applications, by David C. Lay.

2. Signals and Systems, edited by Richard Baranuik. Click for PDF

3. Linear algebra, signal processing, and wavelets. A unified approach. Python version. By Oyvind Ryan. Click for PDF

Instructor: Arie Israel (arie AT math DOT utexas DOT edu)

Office hours: 9:00am-12:00pm, W, RLM 12.126

TA: Carter Smith (cartersmith AT utexas DOT edu)

TA office hours: 9:30-11:30am, T Th, RLM 11.104

First day handout:

syllabus_M340L-ECE.pdf

Schedule:

  • Lecture 1 (08/30)
  • Lecture 2 (09/04)
  • Lecture 3 (09/06)
  • Lecture 4 (09/11)
  • Lecture 5 (09/13)
  • Lecture 6 (09/18)
  • Lecture 7 (09/20)
  • Lecture 8 (09/25):
  • Lecture 9 (09/27)
  • Midterm Exam 1 (10/02)
  • Lecture 10 (10/04)
  • Lecture 11 (10/09)
  • Lecture 12 (10/11)
  • Lecture 13 (10/16)
  • Lecture 14 (10/18)
  • Lecture 15 (10/23)
  • Lecture 16 (10/25)
  • Lecture 17 (10/30)
  • Lecture 18 (11/01)
  • Midterm Exam 2 (11/06)
  • Lecture 19 (11/08)
  • Lecture 20 (11/13)
  • Lecture 21 (11/15)
  • Lecture 22 (11/20)
  • Lecture 23 (11/27)
  • Lecture 24 (11/29)
  • Midterm Exam 3 (12/04)
  • Final Review (12/06): Review
  • Final Exam (12/18): 9-12 PM; Location: CPE 2.214

Lecture 1: Matrix Algebra, Reduced Row Echelon Form

Learning Modules:

  • LM 00a (Vectors and Matrices), LM 00c (Reduced Row Echelon Form), LM 01a (Matrix Algebra)

Notes:

Lecture 2: Linear Systems

Learning Modules:

  • LM 00b (Linear Systems: Gauss Elimination), LM 01b (Linear Systems)

Notes:

Lecture 3: Linear Systems, Invertibility

Learning Modules:

  • LM 00d (Matrix Inverses), LM 00e (Determinants)

Notes:

Lecture 4: Invertibility

Learning Modules:

  • LM 00d

Notes:

Just for fun:

Lecture 5: Vector Spaces

Learning Modules:

  • LM 002

Notes:

Lecture 6: Subspaces and Span

Learning Modules:

  • LM 003

Notes:

Lecture 7: Linear Independence and Bases

Learning Modules:

  • LM 004

Notes:

Lecture 8: Fundamental Matrix Subspaces

Learning Modules:

  • LM004, LM 005

Notes:

Lecture 9: Fundamental Matrix Subspaces

Learning Modules:

  • LM 005

Notes:

Lecture 10: Linear Transformations

Learning Modules:

  • LM 006

Lecture 11: Linear Transformations

Learning Modules:

  • LM 006

Lecture 12: Complex Signals

Learning Modules:

  • LM 007

Notes:

Lecture 13: Inner Product Spaces and Orthogonality

Learning Modules:

  • LM 008

Notes:


Lecture 14: Fourier Series I

Learning Modules:

  • LM 009

Notes:

Lecture 15: Fourier Series II

Learning Modules:

  • LM 009

Notes:

Lecture 16: Orthogonal Projections and Gram-Schmidt

Learning Modules:

  • LM 10a,10b

Notes:

Lecture 17: Gram-Schmidt and QR Decompositions

Learning Modules:

  • LM 10b

Notes:

Lecture 18: Discrete Orthonormal Bases

Learning Modules:

  • LM 11

Notes:

Lecture 19: Discrete Fourier Analysis

Learning Modules:

  • LM 12

Notes:

Lecture 20: Eigenvectors and Eigenvalues

Learning Modules:

  • LM 13

Notes:

Lecture 21: Diagonalization

Learning Modules:

  • LM 14

Notes:

Lecture 22: Markov Chains

Learning Modules:

  • LM 15

Notes:

Lecture 23: Differential Equations

Learning Modules:

  • LM 16

Notes:

Lecture 24: LTI Systems

Learning Modules:

  • LM 17, partial coverage of LM 18
  • LM 19 (optional additional reading on convolutions)

Notes: