 Role
Computational Methods for Data Analysis
(MWF 8:309:20, Lowe 216) Instruction
Professor J. Nathan Kutz
 kutz (at) amath.washington.edu
 2066853029, Guggenheim Hall 414b
 Office Hours: Wednesday 3:305pm in Gug 414B (EDGE: 2066853029)
 
 Teaching Assistant: Xing Fu
 xingf (at) u.washington.edu
 Office Hours: Monday and Friday 35pm in Gug 415L , skype: XXX
 Lectures and Homework
Video Lectures: EDGE (online) , On Campus Students
 Course Notes: 582notes.pdf
 Discussion Board: Catalyst
 Check grades: GRADES
 Homework Dropbox: DROPBOX
 Homework: HW 1 (Testdata.mat) (Due 1/27), HW 2 (music1.wav, music2.wav ) (Due 2/3), HW 3 (derek1.jpg, derek2.jpg, derek3.jpg, derek4.jpg ) (Due 2/15), HW 4 (movies) (Due 2/22), HW 5 (Due 3/9)
 MATLAB: Student Edition (recommended if you do not have access)
 Prerequisites
Solid background in ODEs and familiarity with PDEs and MATLAB, or permission.
 Course Description

Exploratory and objective data analysis methods applied to the physical, engineering, and biological sciences. Brief review of statistical methods and their computational implementation for studying time series analysis, spectral analysis, filtering methods, principal component analysis, orthogonal mode decomposition, and image processing and compression.
 Objectives

How to recognize and solve numerically practical problems which may arise in your research. We will solve some serious problems using the full power of MATLAB's built in functions and routines. This class is geared for those who need to get the basics in scientific computing methods for data analysis. Many of today's major research methods for exploring data analysis will be covered: signal processing, frequency filtering, timefrencency analysis, wavelets, principal component analysis, proper orthogonal decomposition, empirical mode decomposition etc. Applications will range from image processing to characterizing atmospheric dynamics.
 Lecture Notes:
582notes.pdf
 Reference Texts:
 1. D. L. Hartmann, ATM 552 Objective Analysis. (freely available)
 2. L. N. Trefethen, Finite Difference and Spectral Methods. (freely available).
 3. L. N. Trefethen, Spectral Methods in MATLAB. SIAM.
 4. L. N. Trefethen and D. Bau, Numerical Linear Algebra. SIAM.
 5. I. Daubechies, Ten Lectures on Wavelets SIAM (1992).
 (1) Review of Statistics: (1 week)
We will begin with a brief review of statistical methods. The principles of statistics will be largely applied in a computational context for extracting meaningful information from data.
 (a) mean, variance, moments
 (b) probability distributions
 (c) significance testing, hypothesis testing
 (2)
Spectral and TimeFrequency Analysis: (4 weeks)
We will introduce the ideas of signal processing, filtering, timefrequency representations including wavelet expansions. Our application will be largely to problems in image processing, denoising and noise reduction.
 (a) digital signal processing
 (b) noise reduction and filtering
 (c) image processing and face recognition
 (d) timefrequency methods and wavelets
 (3) Objective Analysis Techniques: (5 weeks)
These methods are practical attempts to reduce the dimensionality of the data as well as infer statistically meaningful trends in what otherwise appears to be noisy data.
 (a) Principal Component Analysis (PCA)
 (b) Proper Orthogonal Decomposition (POD)
 (c) Emperical Mode Decomposition (EMD)
 (d) Singular Value Decomposition (SVD)
 Title/author/abstract Title, author/address lines, and short (100 words or less) abstract. (It is not to be a separate title page!)
 Sec. I. Introduction and Overview
 Sec. II. Theoretical Background
 Sec. III. Algorithm Implementation and Development
 Sec. IV. Computational Results
 Sec. V. Summary and Conclusions
 Appendix A MATLAB functions used and brief implementation explanation
 Appendix B MATLAB codes
 Appendix C (optional) Any
algebraically intense calculations
(long and drawn out calculations have no
business in Sec. II!)
 1. Use a professional grade word processor (Latex or MSword, for example)
 2. For equations: Latex already does a nice job, but in Word, use Microsoft Equation Editor 3.0
 3. Label your graphs. Include brief figure captions. Reference the figure in the text with a more detailed account of the figure.
 4. Figures should be set flush with the top or bottom of a page.
 5. Label all equations.
 6. Provide references where appropriate.
 7. All coding should be shuffled to Appendix A and B. Reference it when necessary.
 8. Always remember: this report is being written for YOU! So be clear and concise.
 9. Spellcheck.
Textbook & Notes
There will be no text for this course. I will provide my notes online for you to download (scanned in daily after class). I have several texts and online notes which will be on reserve at the library to look through the different sections.
Syllabus
Grading
Your course grade will be determined entirely from your homework. There will be no exams. Each of the homework sets will be part of your final grade. During the quarter, you will receive roughly weekly homework that you will turn in via the class DROPBOX. These seven or eight homeworks are equally weighted and worth 100% of your grade. This homework should be written as if it were an article/tutorial being prepared for submission. I expect a high level of professionalism on these reports. The following is the expected format for homework submission:
EACH HOMEWORK IS WORTH 10 POINTS. Five points will be given for the overall layout, correctness and neatness of the report, and five additional points will be for specific things that Xing Fu will look for in the report itself. We will not tell you these things ahead of time as a good and complete report should have them as part of the explanation of what you did. For example, in the first homework, Xing Fu may look to see if you talked about the fact that you must rescale the wavenumbers by 2*pi/L since the FFT assumes 2*pi periodic signals. This is a detail that is important, so it would be expected you would have it. If you do, you get the point, if not, then you miss a point.
NOTE: The report does not have to be long. But it does have to be complete.
NOTE 2: This report is not for me, it is for you! Specifically, for the future you. So write a nice report so that you could reproduce the results if you need the methods addressed here in another year or more.
A few things should be kept in mind when generating your reports: