Class Hours

Section A Section B Section C MTWF 9:3010:20
EEB 125
Alexander HornofMTWF 12:301:20
EEB 125
Jacob PriceMTWF 8:309:20
MLR 301
Lowell Thompson  ** Tuesday classes are selfstudying hours for the video lectures.
 Instructional Computing Lab with Matlab Access (Communications Bldg B027)
link
 Tuesday 10:30 am–12:30 pm
 Thursday 12:00 pm–5:00 pm
 Friday 10:30 am–12:30 pm
 Noemail Policy applies for the Instructor and the Teaching Assistants (because of the large number of students)
 Meet with the Instructors and Teaching Assistants in the class and lab hours and raise your questions about the video lectures and homework problems.
 Questions may also be posted on the Piazza Discussion Board but the posts will only be reviewed during working hours.
 DO NOT post code for homework solutions on the discussion board. Only general questions about the videos, lectures, course materials, and clarifications about the homework problems are allowed. If someone could copy it into MATLAB, it will be deleted.
 This course will provide an introduction to the use of computers to solve problems arising in the physical, biological, and engineering sciences. Various computational approaches commonly used to solve mathematical problems (including systems of linear equations, curve fitting, integration, and differential equations) will be presented. Both the theory and application of each numerical method will be demonstrated. The student will gain mathematical judgment in selecting tools to solve scientific problems through inclass examples and programming homework assignments.
 MATLAB will be used as the primary environment for numerical computation. An overview of MATLAB's syntax, code structure, and algorithms will be given. Although the subject matter of Scientific Computing has many aspects that can be made rather difficult, the material in this course is an introduction to the field and will be presented in a simple as possible way. Theoretical aspects will be mentioned throught the course, but more complicated issues such as proofs of relevant theorems/schemes will not be presented. Applications will be emphasized.
 The lectures have been flipped and are available on an open website. Students can enjoy the great advantage of modern technology, being able to watch the lectures anywhere at anytime and make sure that the details of the lectures are well taken.
 Prerequisites: Differential and Integral Calculus, MATH 124 and 125 or equivalent.
Useful Links
 Online Video Lectures
view
Students are required to watch the video lectures before attending the Wednesday classes.  Piazza Discussion Board go
 Scorelator: Online Homework Submission/Grading System go / instructions / Mfile example
 MATLAB and Simulink Student Suite (Recommended if you do not have access) view
 Instructional Computing Lab with Matlab Access (Communications Bldg B027) view
 Textbook "Datadriven modeling and scientific computation" by Prof. Kutz Amazon (NOTE: All author proceeds are forfeit to the Department of Applied Mathematics, University of Washington)
Courseworks and Key Dates
**All dates and grades are subject to change by the instructors at any time. This information is tentative.**
**All dates and grades are subject to change by the instructors at any time. This information is tentative.**
 Homework (Biweekly) (60% of total score)
The lowest homework score will be dropped.
Homework 1 (due Apr 6, 2015, Monday, 4 pm)
 Homework 2 (due Apr 20, 2015, Monday, 4 pm)
Update for Homework 2: Question 2c has been clarified.
 Homework 3 (due May 4, 2015, Monday, 4 pm)
salmon data Use "save link as" to download.
 Homework 4 (due May 18, 2015, Friday, 4 pm)
 Homework 5 (due Jun 1, 2015, Monday, 4 pm)

Homework 1 (due Apr 6, 2015, Monday, 4 pm)
 Quizzes (Biweekly, Friday, in class) (20% of total score)
 Quiz 1 (Apr 10, 2015, Friday)
 Quiz 2 (Apr 24, 2015, Friday)
 Quiz 3 (May 8, 2015, Friday)
 Quiz 4 (May 22, 2015, Friday)
 Final Exam (20% of total score)

Section A Section B Section C Wednesday, June 10
8:3010:20
EEB 125Thursday, June 11
8:3010:20
EEB 125Tuesday, June 9
8:3010:20
MLR 301

 The grade scale varies for different classes in different terms with different instructors.
 In the past, final grades have been curved based on the total scores.
 E.g., in the 2015 Winter Term instructed by Dr. KingFai Li, the following
grade scale was adopted:

Total Score Approx. Grade 95 – 97 3.9 90 – 92 3.5 80 – 84 3.0

 Final scores will probably follow roughly the same scale this quarter, but the decision is ultimately up to each instructor.
Instructors
 Section A:
Alexander Hornof
Office Hours: Monday and Wednesday classes
 Section B:
Jacob Price
Office Hours: Monday and Wednesday classes
 Section C:
Lowell Thompson
Office Hours: Monday and Wednesday classes
 Peter Sentz
Office Hours: Friday classes, Sections B and C
Lab Hours: Tuesday 10:3012:30, Thursday 12:002:30  Brian De Silva
Office Hours: Friday classes, Sections A and C
Lab Hours: Thursday 2:305:00, Friday 10:3012:30.
 TBD
Syllabus

Week 1 Basics of MATLAB and Introduction  Video Lectures 1–3
 Constructing matrices/vectors (text 1.1)
 for and if statements (text 1.2)
 Inputing/Exporting/Plotting data (text 1.5)
 Matlab function fzero
Week 2 Linear Algebra and Direct Solutions of Ax=b  Video Lectures 4–6
 Matrix manipulations (notes)
 Gaussian elimination (text 2.1)
 LU decomposition lu (text 2.1)
 The "blackslash" command x=A\b.
Week 3 Iterative Methods for Ax=b when A is large and sparse  Video Lectures 7–9
 Jacobi iteration (text 2.2, notes, L07_testJacobiFAILS.m, L07_testJacobiWORKS.m)
 eigenvalues and eigenvectors eig (text 2.4, notes)
 Jacobi and GaussSeidel iteration (text 2.2, notes, L09_testEigJacobi.m, L09_testGaussSeidelNaive.m, L09_testGaussSeidelFast.m, Jacobi.m, GaussSeidel.m)
 Matlab function bicg
Week 4 Interpolation and Curve Fitting of Empirical Data  Video Lectures 10–12
 Least squares (text 3.1)
 Interpolation and polynomial fitting (text 3.2)
 Data fitting in Matlab (text 3.3)
 interp1, spline, and polyfit
Week 5 Optimization  Video Lectures 13–15
 Unconstrained optimzation: Golden Search and fminsearch (text 5.1, 5.2)
 Linear programming (text 5.3)
 Genetic algorithms (text 5.5)
 linprog and ga (genetic_demo.m)
Week 6 Numerical Differentiation and Integration for Functions and Data  Video Lectures 16–18
 Forward/Backward/Central differences approximations and error estimation.
(text 4.1, 4.3, notes, TaylorSeries.m, FiniteDifference.m)  Highorder derivative. Left/Rightrectangle rule for integration. (text 4.2, 4.3, notes, numdiffc.m)
 Trapezoid and Simpson's rule and errors.
Integrating particle in vector field.
(text 4.2, 4.3, notes, numintc.m)  Lecture Notes by Li.
Week 7 Introduction to Differential Equations  Video Lectures 19–21
 Forward and backward Euler integration. Springmassdamper system.
Try ode45.
(text 7.1, 7.2, notes, L19_SpringMassDamper.m)  Error analysis and stability. Example: damped pendulum.
Use ode45.
(text 7.1, 7.2, notes, L20_simpend.m, pend.m)  2nd and 4thorder RungeKutta method.
(text 7.1, 7.2, notes by Brunton, notes by Li)
Week 8 Advanced Differential Equations  Video Lectures 22–24
 Write our own RK4 timestepper and compare with ode45 on the Lorenz equation.
(notes, L22_simulateLORENZ.m, rk4singlestep.m, lorenz.m)  Integrate a cube of points through Lorenz. Vectorize integrator (1001000X speedup). Sensitivity and chaos. (notes, rk4singlestep.m, lorenz3D.m, L23_simLorenzSLOW.m, L23_simLorenzFAST.m)
 Chaos. Examples: double pendulum, threebody problem, Lorenz. Symplectic and variational integration. (notes, double pendulum notes, variational.cc, rungekutta78.cc, doublegyreVEC.m, integrateDGgood.m)
 Lecture Notes by Li. flame.m.
Week 9 The Singular Value Decomposition
Transforming a matrix of data into coordinates that emphasize the dominant features Video Lectures 25–27
 Theory and Implementation (text 15.1)
 Principal components analysis (text, 15.3)
 Demo: eigenfaces
(text 2.5, EIGENFACE.m, faces.zip)  Lecture Notes by Li.
Week 10 Fast Fourier Transforms  Video Lectures 28–30
 Discrete Fourier Transform (DFT) basics (text 10.1, 13.1, notes)
 Fast Fourier Transform (FFT) and filtering, audio (notes, EX1_FFT.m, EX2_FFT.m, loadMusic.m)
 FFT and image compression (notes, compress_wFFTcontour.m)
 Lecture Notes by Li.
MATLAB on/off campus
 Some departments may provide Matlab access to their students. Please check with your department.
 There is MATLAB access at the ICL on campus in the Communications building room B022. You can also access this lab remotely. info
 MATLAB and Simulink Student Suite can be purchased from the University Bookstore or online Mathworks.com for a student rate of $99.
 Some addon products for image processing will be used in this course.
 Homework will be submitted and graded online through a system called Scorelator. Instructions
 LATE HOMEWORK WILL NOT BE ACCEPTED.
IMPORTANCE: UW only has licenses for 40 simultaneous MATLAB users. On high volume time, especially when the homework is due, the terminal may be busy for hours and your submission will be queued. To avoid late submission due to traffic, you are strongly recommended to submit your assignment as soon as possible.  You have up to 5 attempts per homework to get everything correct. If everything is correct the first time a homework is submitted, you will receive a 100% for that homework. If something is not correct, then you may fix it and resubmit. Your best score for each homework will be your recorded grade (i.e. there is no penalty for correcting and resubmitting).
 An AntiCheat system is enforced to compare your code against the codes of others in your section and other sections, and all past years.
 Note:
 Scorelator must be accessed through Firefox or Internet Explorer (NOT Safari or Chrome). Additionally, Scorelator CANNOT be accessed on the ASLAB terminal server.
 The instructor will register your UW NetID to Scorelator after the first week of class, given that you have registered the class before the beginning of the quarter.
 The default login name is (UW NetID)@u.washington.edu. DO NOT use "@uw.edu".
 An email containing login information will be sent to you UW email box. Be sure to check if any emails entitled "Scorelator" are filtered/spammed.
 If no email has been received, go to the Scorelator homepage, type in your UW email address and click on: "I forgot my password". Another email containgin new login information will be sent.
 If you have registered after the beginning of the quarter and the procedure above does not work for you, inform your instructor and he/she will add you to the system.
 Scorelator has become an open source, free program since January 1, 2015.
 DO NOT send any emails to Scorelator. Talk to your instructor and TAs for any questions.
Course Calendar