GEOP 529/Math 519 Overview and Syllabus

Spring Semester, 2009

M/W/F 11:00 - 11:50 AM, MSEC 351

 

Professors:

 

      Rick Aster (RA), x5924, MSEC 356; aster@ees.nmt.edu

       Brian Borchers (BB), x5813; Weir 222; borchers@nmt.edu

 

Note that this is a co-taught course, and both professors may not necessarily be present for all lectures due to travel and other obligations. You are encouraged to meet with either professor when you have questions (this may be best accommodated by email and/or discussion before class). Dates when each professor expects to be out of town are indicated on the syllabus below to the best of our ability.

 

Class Web Site:  http://www.ees.nmt.edu/Geop/Classes/GEOP529.html

 

Text:  Parameter Estimation and Inverse Problems, by Aster, Borchers, and Thurber, Elsevier Academic Press, 2004.   Note that our textbook will essentially function as the notes for the class (although we will add ancillary material from time to time).  There is an accompanying book web site that includes errata.

 

Book Web Site: http://www.ees.nmt.edu/Geop/Classes/GEOP529_book.html. 

 

Overall Grading Distribution:

 

 

Student Presentations.  A major component of this class is an ~20-minute class presentation (plus ~5 minutes for questions) and accompanying concise written report on an inverse or parameter estimation problem that is of particular interest to you.  If your talk uses data, you may use either a real data set from your research or from elsewhere, and/or generate and analyze (meaningfully realistic) synthetic data.  We alternatively welcome theoretical or implementation talks on specific techniques.

 

Professors Rick Aster and Brian Borchers will be available throughout the semester to assist you with presentation topic suggestions, references, and details on technical implementation. 

 

In your presentations (as well as generally, of course) it is crucial that you convincingly demonstrate your knowledge of the material through summarizing the context and importance of your topic, insightful understanding and fielding of questions, citing of references, and by applying your own unique perspective.  Do not simply reiterate someone else's work from the literature!! Hopefully needless to say, be certain that you do not duplicate anything verbatim from any of your sources without direct attribution! This is plagiarism and can result in your flunking the course.  See us if you have any questions about what constitutes plagiarism.  The NMT Library also has a useful link on avoiding inadvertent plagiariasm.

 

For further clarification, check out the NMT Library Plagiarism Link:  http://www.nmt.edu/~nmtlib/INFO/ORef/plagiarism.html

 

Project Reports and Project Deadlines.  Your report should be approximately 7-10 pages long and should encapsulate the gist of your project without excessive elaboration, and must feature your own original observations and comments.  You are encouraged to use MATLAB and Powerpoint in your presentation.  Be certain to have your talk well rehearsed (students are welcome to help each other here and/or show their slides to the professors in advance) and your presentation hardware and software is working well before class starts so that your audience doesn't have to wait for you to get set up.  There is a computer-projector projector installed in the MSEC 351 classroom that you should be well familiar with and test with your laptop of choice before giving your talk.  You have the option of handing out ancillary notes to the class on the day of your presentation if you feel that it will enhance the lecture.  We strongly encourage you to begin planning your project immediately and to keep this assignment in mind throughout the course.  The dates for specific presentations will be scheduled in association with the professors.  The class web site also has numerous examples of past class projects.

 

Special dates to remember:

 

 

Using MATLAB.  It is essential that you become proficient in MATLAB to do well in this course! MATLAB is a very useful and simple to use package that is available on the Geophysics Program Suns and Macs, on TCC-supported machines, and is also available in a low-cost student PC version.  If you don't already have a facility with MATLAB, please start familiarizing yourself (e.g., by running the demo program) as soon as possible.  We will also spend up to four lectures early on in the course reviewing MATLAB and its linear algebra capabilities.  There is also a MATLAB primer posted on the class web page, and of course, there are many resources on-line.

 


 

 

2009 Spring Syllabus (Subject to Minor Changes)

All reading assignments are from Aster, Borchers, and Thurber, 2004)

 

Linear Algebra.  Reading: Chapter 1; Appendix A

W         1/21      Class Overview and Introduction                RA

F          1/23      Class Overview and Introduction                RA

M         1/26      Linear Algebra Review w/MATLAB          RA

W         1/28      Linear Algebra Review w/MATLAB          RA

F          1.30      Linear Algebra Review w/MATLAB          RA

M         2/2      Linear Algebra Review w/MATLAB           RA

 

Probability and Random Variables.  Reading:  Appendix B

W         2/4      Probability Review                                  RA      

F          2/6        Probability Review                                 RA       1st HW Due

 

Linear Regression.  Reading:  Chapter 2

M         2/9        Least Squares Theory                             RA

W         2/11        Least-squares Theory                           RA

F          2/13        1-norm Theory                                       BB

M         2/16      1-norm Theory                                        BB

 

Discretizing Continuous Inverse Problems.  Reading:  Chapter 3

W         2/18      Discretization                                         RA

F          2/20      Discretization                                          RA       2nd HW Due Deadline for presentation topic approval, clear with either RA or BB

M         2/23      Tomography                                           BB       (RA Gone)

 

Rank Deficient and Ill-posed Problems.  Reading:  Chapter 4

W         2/25      Rank Deficient Problems                     RA      

F          2/27      Rank Deficient Problems                      RA

M         3/2       Ill-posed Problems                                 RA

W        3/4       Ill-posed Problems                                   RA

 

Regularization and Iterative Methods.   Reading:  Chapters 5, 6, 7

F          3/6        Regularization                                         BB       (RA Gone) Deadline for presentation Abstract and Outline, present to both RA and BB

M         3/9      Spring Break

W         3/11      Spring Break

F          3/13      Spring Break   

M         3/16        Regularization                                        RA       (BB Gone); 3rd HW due

W         3/18        Regularization                                        RA       (BB Gone)

F          3/20        Regularization                                         BB

M         3/23      Discretization, revisited                           BB       (RA Gone)

Nonlinear Regression.  Reading:  Chapter 9

W         3/25      Nonlinear Regression                               BB      

F          3/27      Nonlinear Regression                               BB       4th HW due

M         3/30      Nonlinear Regression                              BB

W         4/1        Nonlinear Regression                               BB       (RA Gone) Deadline for Full draft of final report, present to both RA and BB

 

Nonlinear Inverse Problems. Reading:  Chapter 10

F          4/3        Nonlinear Inverse Problems                      BB      

M         4/6        Nonlinear Inverse Problems                     BB      

W         4/8      Nonlinear Inverse Problems                       BB       (RA Gone)

F          4/10      Holiday

M         4/13      Nonlinear Inverse Problems                     BB

 

Introduction to Bayesian Methods. Reading:  Chapter 11

W         4/15      Bayesian Inverse Theory                           BB       (RA Gone)

F          4/17      Bayesian Inverse Theory                           BB       (RA Gone); 5th Homework due

Introduction to Markov Chain Monte Carlo and other Global Methods. Reading:  New Notes

M         4/20      Global Methods                       BB

W         4/22      Global Methods                       BB

F          4/24      Global Methods                       BB 

M         4/27      Global Methods                       BB (RA Gone)

W         4/29      Student Presentations

F          5/1      Student Presentations              6th Homework due

M         5/4      Student Presentations

W         5/6        Student Presentations                              

F          5/8        Student Presentations               Last day of classes; Project Write-ups Due