AMATH 352 A: Applied Linear Algebra And Numerical Analysis

AMATH 352 A: Applied Linear Algebra And Numerical Analysis

Professor: Niall Mangan, niallmm at uw dot edu
Office hours: Mon 3-4pm (Lewis 212), Thurs 9:00-10:00am (Lewis 116), Friday 1:30-2:30pm (Lewis 116)
Teaching Assistant: Tommaso Buvoli, buvoli at uw dot edu
Office hours: Mon 4-6 (Lewis 212), Tues. 3-5pm (Lewis 115)
Grader: Cosmin Naziru 
Email policy: We will not answer questions about problem sets, quizzes or coding via email. Please use the discussion board  (do not post answers or working code) and office hours.
We Meet:  MWF 12:30-1:30 in CMU 120
Prerequisites: Math 126 or  Q SCI 293
Schedule: 
  • For detail schedule of course topics and assignments see Modules page.
  • Midterm: Wednesday, November 9, 2016, in class (12:30-1:20)  in CMU 120
  • Final: Thursday, December 15, 2016 8:30-10:20 in CMU 120
  • UW Academic Calendar

Homework Policy and Grading

Grades will come from the following:

  • Problem Sets: 7 problem sets for 40 points each. 
  • Online Quizzes: 7 quizzes for 20 points each. 
  • Midterm and Final exams: 100 points each
  • Participation in online discussion board: 30 points.
  • Total: 650 total points.
Problem sets will consist of 2 parts due on Wednesdays:
  • Matlab code submitted to scorelator, an automated grading program. You will have 5 chances for submission. Final submission counts. Scorelator has built in plagiarism detection. Scorelator Resources
  • Non-coding problems submitted to canvas. These problems may be written up by hand and scanned in, or typeset using LaTex. (LaTeX Resources) Please save as a pdf.
Online Quizzes will be conceptual questions related to the homework set for the week due on Thursdays. They are designed to answer while/after completing the problem set. You will have 2 chances for submission, and the  system will average your scores.

Course Description:

Nearly every discipline with a quantitative component including engineering, physical sciences, social sciences, finance, computer graphics, big data, and machine learning rely on linear algebra. Numerical computation greatly enables modeling the data analysis in these fields. In order to utilize linear algebra and computing for problem solving, it is essential to understand how to set up problems (in the linear framework and numerically), determine when well defined solutions exist, write programs and algorithms in MATLAB to solve these problems, evaluate whether the algorithm will find the solution efficiently, and evaluate the accuracy of the computation. For a detailed list of topics see the modules.

Textbook & Resources:

No textbook will be required for this course. We will supply class notes in each module.

My notes will be mostly based on Prof. Hetmaniuk Notes for Linear Algebra and Prof. Nathan Kutz notes for numerical analysis, but I will skip around, deviate, and provide my own notes.
Here are a few other freely available texts I may also reference:

Matlab Resources

Matlab/Ocatave Resources: You need to obtain a copy of or access to MATLAB. You can get it from a variety of sources:

Group work and Academic Honesty policy: 

You are encouraged to discuss and work in groups to solve problem sets. 
 
You must write up your own solution and your own code. Copy, pasting, and editing will be considered plagiarism. Do not be a cheater, it does not help you learn the material and I will have you do something harder to make up the grade, give you a zero, and/or report you for academic misconduct depending on the situation.
 

Please read the UW policy here. By staying registered in the class you indicate your acceptance of all its terms. We do not accept late homework or absence without official reasons (medical, etc.) approved by a student dean. If you miss class, please coordinate with colleagues to find out what you missed (do not email the professor to help you catch up). 

Course Summary:

Date Details