• Where is the discussion forum?
• Where are my grades listed?
• At the top, under Student->View Grades
• When are the exams? Exam Schedule
• Can I use something other than Python? No.
• Do I need to type the homework? No, but it must be readable. You will lose points for bad handwriting or unreadable scans.

## Exam Schedule

• Midterm 1: Sunday September 27 - Wednesday September 30
• Midterm 2: Sunday November 1 - Wednesday November 4
• Final: tentatively Thursday December 10 - Monday December 14, but do not make plans around this yet since this is not confirmed

## Computing with Data

Applications

• Computation cost on a cloud and the benefits of fast data structures
• Timing Data from an experiment
• Floating point disasters
• Image Manipulation
• Randomness and simulating risk
• Options Pricing
• math.h and Taylor series

Objectives

• Use numpy for more efficient computations
• Construct examples that show the benefits of contigous memory (numpy arrays)
• Identify the advantages of numerical libraries
• Create a numerical experiment that measures the cost of basic array operations
• Represent a real number in a floating point system
• Measure the error in rounding numbers in the IEEE-754 floating point standard
• Compute floating point vaules in different portions of the floating point range
• Use the IEEE-754 standard to quantify error floating point arithmetic
• Compute the memory storage of single versus double precision
• Describe the accuracy of several examples in terms of machine epsilon
• Determine whether data grows algebraically versus exponentially and at which rate
• Design a numerical experiment with reproducibility, randomness, consistent timing in mind
• Compute a random process
• Use randomness to confirm a numerical hypothesis
• Approximate a function using a Taylor series approximation
• Quantify the error in using a Taylor series approximation at several points
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

Applications

• Raw Materials and Manufacturing
• Algebraic Connectivity of Graphs
• Prinicple Component Analysis on a dataset
• Page Rank and similar probability measures
• Image Compression
• Learning Algorithms

Objectives

• Take a linear algebra operation and apply the operation in a computational setting
• Represent a problem description in terms of matrices and vectors
• Load a collection of data into matrix and vector data structures
• Compute the solution to a linear system of equations
• Detail the pieces of an LU factorization of a linear system
• Use an LU factorization to solve a problem with many right-hand sides
• Measure the number of digits of accuracy for poorly conditioned problems
• Compute eigenvalues/eigenvectors for different applications
• Use the Power Method to find a specific eigenvector
• Compute singular value decomposition for different applications
• Use singular values to identified important components
• Represent a linear system as a sparse linear system
• Represent a graph as a sparse system
• Compute the cost of a sparse matrix-vector multiply
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

Applications

• Polling Data
• Baseball Statistics and Trends
• Scalable fonts

Objectives

• Interpolate or fit a piece of data with a given model (or function)
• Find the derivative of noisy data
• Compute the intgral of a data set
• Use least-squares data fit to determine a trend
• Calculate the the cost of a least-squares solution
• Measure the error in a least-squares solution
• Approximate the minimum to an objective function
• Approximate the minimum to an objective function in multiple dimensions
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.

• Quizzes will allow two graded attempts. This higher grade will be recorded.
• Quizzes are to be done on your own, not in groups.
• Quizzes will have a deadline of 10 minutes before class. After this you can take the quizes for half-credit for up to 3 days. You must "end session" and "confirm" before the deadline; if you start a quiz before the deadline, but finish after the deadline, then you will receive half-credit. If you submit after 3 days, then no credit will be given.
• You must fully submit your entire quiz in order to receive credit. To do so, you must press the "Submit for grading" button in the upper right corner. Note, this button was previously titled "End session". This is separate from submitting a final answer or saving an answer to a single question. Failure to submit the entire quiz, regardless of whether answers were saved for individual questions, will result in no credit.

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:

• Homeworks will have one graded attempt. Meaning if you submit your solutions, then you cannot change them for regrading.
• Homework submitted after the deadline will receive half-credit (ie, your work will be graded and multiplied by 0.5). You can submit homework for half-credit for up to 7 days. You must "end session" and "confirm" before the deadline; if you start a homework before the deadline, but finish after the deadline, then you will receive half-credit. If you submit after 7 days, then no credit will be given.
• Solutions to the homework can be viewable (and reviewable) after the due date. If you are submitting homework for the half-credit due date, you cannot directly copy the sample solution. What you submit must still be written by you, not just copied.
• A homework is considered either all late or all on-time. If any portion of the homework is submitted after the deadline then the entire homework will be graded as late.

# 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.

# Accomodations

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

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

### Detailed Distribution:

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)

## 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.

### 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.