Numerical Analysis (CS 450) Fall 2020
| What | Where |
|---|---|
| Lecture (via Zoom) | Tu/Th 11:00am-12:15pm CT (UTC-5 to Nov 1, UTC-6 from Nov 1) |
| Class URL | https://relate.cs.illinois.edu/course/cs450-f20/ |
| Instructor | Luke Olson |
| Quick Links |
|
Learning Goals for CS 450
- Analyze the conditioning of common numerical problems such as solving a linear system, finding eigenvalues, numerical differentiation and integration, etc.
- Calculate numerical approximations to solutions to linear and nonlinear systems, eigenvalues/eigenvectors, optimization problems, integrals, derivatives, and solutions to differential equations.
- Compare the accuracy and cost of different numerical methods for solving a numerical problem.
- Estimate the accuracy and efficiency of numerical approximations.
- Develop code to solve numerical problems.
- Design numerical experiments to test various numerical methods.
FAQ
- Q -- What is the format of the lectures?
Lectures will be delivered through Zoom (link below) from 11-12:15 Tu/Th. It is best to arrive say 5 minutes beforehand -- the lectures will intend on starting promptly at 11. They will include a combination of demos, hands-on activities, and discussion.
- Q -- Are lectures recorded?
Yes! All lectures will be posted on MediaSpace
- Q -- Why is chat is not enabled in Zoom?
We'll be using the #lecture room in Campuswire for discussions during lecture.
- Q -- We're using so many different platforms, why?
With everything online, there's a need to chat and discuss~---~we've selected Campuswire as it adds functionality of both Slack/Discord and Piazza, as well as adding FERPA compliance. Relate will be your main source of content, delivering homework, quizzes, and exams, as well as hosting your grades.
- Q -- What happens if my internet drops during lecture?
We'll have recorded videos. If Zoom goes down with enough warning, then we'll turn to a service such as Jitsi.
- Q -- Where do we take exams?
Exams will be proctored online, through the Computer Based Testing Facility (CBTF).
- Q -- When are quizzes due?
Quizzes are due before 11am on the day of the lecture.
- Q -- There is no box asking what to do with my quiz at the deadline?
Quizzes are not auto-submitted. You must click the green check mark and fully submit your quiz to receive credit.
- Q -- When is homework due?
Homework is due before 5pm each week (homework is due Friday).
Exam Scheduling
-
Add CS450 as a course in the CBTF scheduler
-
Schedule a time slot during our given exam window or register for a conflict exam time
Team
Shelby Lockhart
(Teaching Assistant)
Email: None
Office Hours: T/Th 9:00 CST, W 8:00 CST
Yuchen Pang
(Teaching Assistant)
Email: None
Office Hours: M/Th 13:00 CST, W 9:00 CST
Linjian Ma
(Teaching Assistant)
Email: None
Office Hours: M 15:00 CST, T 13:00 CST, W 21:00 CST
Lukas Spies
(Teaching Assistant)
Email: None
Office Hours: M 14:00 CST, T 10:00 CST, W 04:00 CST
Textbook
Michael T. Heath, Revised Second Edition, Society for Industrial and Applied Mathematics
Computing
We will be using Python with the libraries numpy, scipy and matplotlib for in-class work and assignments. No other languages are permitted. Python has a very gentle learning curve, so you should feel at home even if you've never done any work in Python.
Running Code on your Own Computer
While running code in relate system should technically suffice to do your work for this class, you may find it useful to also install Python on your own computer.
The recommended and perhaps one of the easier ways of doing so involves downloading the Anaconda Python distribution. Note that this is a commercial product (even if it is free of charge), and this is not intended as an endorsement of the company or the product. Note that we cannot promise to provide technical support for this installation.
Another way to obtain a Python installation is through a virtual machine image:
Python Help
(see section 1 of the outline for more)
- Python tutorial
- Facts and myths about Python names and values
- Learn Python the hard way
- Project Euler (Lots of practice problems)
- From Python to Numpy
Python workshop material
- Video: Located on Echo 360 along with the other class recordings
- Tutorial material
- Scipy lecture notes
- CSE workshop training material
Numpy Help
(see section 1 of the outline for more)
- Introduction to Python for Science
- The SciPy lectures
- The Numpy MedKit by Stéfan van der Walt
- The Numpy User Guide by Travis Oliphant
- Numpy/Scipy documentation
- More in this reddit thread
- Spyder (a Python IDE, like Matlab) is installed in the virtual machine. (Applications Menu > Development > Spyder)
- An introduction to Numpy and SciPy