 Role
Computational Methods for Data Analysis
(AMATH 582  MWF 8:309:20, Lowe 216)
(AMATH 482  MWF 9:3010:20, BAG 261) Instruction
Professor J. Nathan Kutz
 kutz (at) uw.edu
 2066853029, Lewis 207
 Office Hours: Wednesday 45:30pm, Thursday 810am (EDGE call 2066853029)
 
 Teaching Assistant: Krithika Manohar and Susie Sargsyan
 amath582 (at) uw.edu
 Office Hours: Tuesday 25pm (Lewis 115) and Thursday 10amnoon (Lewis 128/129)
 EDGE (section B) Office Hours: Tuesday 10:3011:30 and Thursday 12:301:30
 Lectures and Homework
Video Lectures: Videos
 Course Notes: 582notes.pdf or book: Amazon
 Discussion Board: Catalyst
 Check grades: GRADES
 Homework Dropbox: DROPBOX
 Homework: HW 1 (Testdata.mat) (Due 1/22), HW 2 (music1.wav, music2.wav) (Due 2/5)
 MATLAB: in person or remotely at ICL OR ( 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, equationfree methods for complex systems, compressive sensing, 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.
 (1) Review of Statistics: (0 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
 (e) sparse representation and compressive sensing
 (3) Dimensionality Reduction and EquationFree Techniques: (6 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) Singular Value Decomposition (SVD)
 (d) Dynamic Mode Decomposition (DMD)
 (e) Model Reduction
 (f) Multiscale equationfree methods
 (g) Clustering and classification
 Midterm  February 12, 2015
 Final  March 5, 2015
 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.
Syllabus
Grading
Your course grade will be determined entirely from your homework (60%) and two takehome tests (a midterm (20%) and final (20%)).
Each of the homework sets will be part of your final grade. During the quarter, you will receive five homeworks that you will turn in via the class DROPBOX. These five homeworks are equally weighted and worth 60% 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 the TAs 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, the TAs 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: