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Exam Schedule

Computing with Data



Lecture Date Topic Material
1 Tue Aug 25 Computation with Python!
2 Thu Aug 27
3 Tue Sep 01 Why are there so many errors?!
4 Thu Sep 03
5 Tue Sep 08 Designing a numerical experiment
6 Thu Sep 10 Randomness and Simulation. Monte Carlo
7 Tue Sep 15 An approximation tool. Taylor Series
8 Thu Sep 17 An in-class project. Options Pricing with Monte Carlo

Computing with arrays of data



Lecture Date Topic Material
9 Tue Sep 22 Linear Algebra Meets Computation
10 Thu Sep 24 Linear Algebra Meets Computation
11 Tue Sep 29 no class.
12 Thu Oct 01 Norm
13 Tue Oct 06 Solving Systems
14 Thu Oct 08 Factorizations and Eliminations
15 Tue Oct 13 Pivoting and Computational Expense and Accuracy
16 Thu Oct 15 Eigen Everything and Singular Values
17 Tue Oct 20 The Power Method and Ranking Things
18 Thu Oct 22 Singular Values and Compression
19 Tue Oct 27 Orthogonality
20 Thu Oct 29 Your Graph is a Sparse Matrix

Approximating data



Lecture Date Topic Material
21 Tue Nov 03 no class
22 Thu Nov 05 Finding Least-Squares
23 Tue Nov 10 More Least-Squares
24 Thu Nov 12 Case Study. Cancer data and prediction
25 Tue Nov 17 Spaces of Things
26 Thu Nov 19 Newton's Method
27 Tue Nov 24 Thanksgiving Break
28 Thu Nov 26 Thanksgiving Break
29 Tue Dec 01 Optimization
30 Thu Dec 03 Optimization
31 Tue Dec 08 Exam Review

Office/Discussion Hours

Day Time Type (Location) People
Mon 11-12:30pm OH (0209 Siebel) Nathan Bowman
Tues 10:45-11:30am OH (4312 Siebel) Luke Olson
Wed 10:30-12:30pm OH (0209 Siebel) Erin Carrier/Pete Sentz
Wed 4:30-6pm Discussion (1109 Siebel) Ryne Beeson/Erin Carrier
Thur 4:30-6pm Discussion (1103 Siebel) Pete Sentz/Nathan Bowman

The lectures will have associated pre-lecture material: a collection of videos, slides, short write-ups, etc that will support the upcoming lecture.

Quiz Policy

As part of the pre-lecture material, there will often be associated quizzes. These quizes will be short and directly related to the latest material.

Importantly, the quizzes are designed to help you go through pre-lecture material! They are not a major component of the grade, but should be a helpful step toward understanding the course content.

Homework Policy

Homeworks will be released on weekly basis, being released on Fridays and due on Fridays at 4pm (leaving approximately one week to complete).

There are a few rules that are different from quizes:

Exam Policy

There will be three exams: two midterm exams and a final. The final will be comprehensive but weighted toward the last section of material in the course.


If you have requests for special accomodations in the classroom, with the digital content, or for exams, please see Prof. Olson at


HW 25%
Exam1 20%
Exam2 20%
Final 25%
Quiz 10%

Detailed Distribution:

Academic Grading System in the US

Course Grade Total Score as weighted above
A [93,99)
A- [90,93)
B+ [87,90)
B [83,87)
B- [80,83)
C+ [77,80)
C [73,77)
C- [70,73)
D+ [67,70)
D [63,67)
D- [55,63)
F [ 0,55)

Python Help

Numpy Help


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.

Virtual Machine Image

While you are free to install Python and Numpy on your own computer to do homework, the only supported way to do so is using the supplied virtual machine image.

Download Virtual Machine »