MATH 4990
Mathematical
Models and Their Analysis
- MATHEMATICA
resources
- While some of the homework problems will require the use
of symbolic calculations, most of them (if not all) can be done online
via publicly available Wolfram Alpha.
- Another way to avoid installing Mathematica is to log into
its Cloud version: https://www.wolframcloud.com/. Sign in using your
UVM email (use the option Sign in
with Single Sign-on (SSO)) and
then, at a prompt, UVM password and then open a new Notebook.
- If you prefer to download a copy of Mathematica, go
to the UVM
Software Portal,
find Mathematica, and follow your nose to install it (you will need to
enter your UVM netID and password). Between the two download options,
you can choose Manager vs Full Installer, as the former is a bit faster.
After installing, the system will ask you to activate Mathematica if you don’t already have an active
license on your computer. Choose Activate Through Your
Organization (SSO), enter your UVM email and follow your nose.
- There is a new feature in Mathematica 14, called
"Free-form output," available both in an installed copy of the program
and in WolframCloud (but not in Wolfram Alpha). To see how it works, at
the input begin typing something like "solve cos x + sin x = 0.5" or
"find root of x^2-1" (without quotes). A drop-down menu will appear,
one of whose options is "Convert to free-form output." Select it and
see what happens.
- Instructions
how to install MATLAB
- Run the Installer and then follow your nose (accept the
License agreement etc; your/UVM's license number is 1093983).
- Closer to the end of the installation, Matlab will ask
you
which Toolboxes you want to install. You don't want to install all of
them or even "too many," since they will take up your disk space. A
subset
that should more than cover your needs in this course is:
- Control
System
Toolbox
DSP System
Toolbox
Global Optimization Toolbox
Image Processing
Toolbox
Optimization
Toolbox
Signal Processing Toolbox
Symbolic Math Toolbox
- If you wish, you may watch this walk-through video with
instructions:
https://www.mathworks.com/videos/install-matlab-on-personal-computers-campus-wide-license-1717472837232.html
- The installation process takes over 30 minutes.
- MATLAB
Primer by
K. Sigmon
- MATLAB
tutorials by E. Neumann:
- MATLAB
Primer by S. Nakamura. This
is Chapter 1 from the book "Numerical Analysis and Graphic
Visualization with MATLAB," 2nd ed.
- Materials for
the Midterm Project:
- Project's title: Projections, Singular Value Decomposition, and some of their Applications
- A breakdown of the Project into Topics (you should ignore Topic 8)
- Each presenter will have 20 minutes for their presentation. You should have no more than 15 slides for that amount of time.
- A sample final presentation from a previous year (I don't have any midterm samples). Also, your presentation must have a title slide.
- Background
information from Linear Algebra
- Resources
- The
resources listed below should be sufficient for you to prepare a
presentation on any of the Topics listed above. However, you may
use any other resources, including AI, Wikipedia, Google, etc. The
bottom line is that you understand your Topic and present it clearly.
- Selected pages from D. Lay et al, Linear Algebra and its applications, 6th Ed.
- Selected pages on Projections from D. Poole, Linear Algebra, a modern introduction, 4th Ed.
- Selected pages on Least Squares from D. Poole, Linear Algebra, a modern introduction, 4th Ed.
- Selected pages on SVD from D. Poole, Linear Algebra, a modern introduction, 4th Ed.
- Selected pages from S. Leon, Linear Algebra with applications, 9th Ed.
- Selected pages from B. Noble & J. Daniel, Applied Linear Algebra, 3rd Ed.
- My handwritten notes on Spectral Decomposition of a symmetric matrix.
- My handwritten notes on SVD.
- Selected pages from M. Embree’s Working notes on Linear Algebra.
- D. Kalman, “A Singularly Valuable Decomposition: The SVD of a matrix,” (2002).
- Image matrix X and a Matlab file loading it.
- A. Falini, “A review on the selection criteria for the truncated SVD in Data Science applications,”
J. Comput. Math. Data Sci., 5, 100064 (2022).
- A GitHub page on Truncated SVD by Ilias Bilionis. PDF
- J. Simatupang, “Noise Reduction in Satellite Imagery Using Singular Value Decomposition,” (2024). PDF
- Blog entry of Mat Kelcey on Latent Semantic Analysis and SVD (2009). PDF
- Notes by Alex Thomo on Latent Semantic Analysis. PDF
- T. Landauer, P. Foltz, D. Laham, “Introduction to Latent Semantic Analysis,” Discourse Processes, 25, 259–284 (1998). PDF
- Wikipedia’s entry on ‘Latent Semantic Analysis’.
- Topic # Name Rehearsal time (week of March 3)
- 1 Jackson N Th 10 - 11:15
-
2
Will
T 2:00 - 3:15
-
3
Max
W 11:00 - 12:15
-
4
Grace
Th 12:30 - 1:45
- 5 Jackson H T 12:45 - 2:00
-
6
Nessa
W 12:15 - 1:30
-
7
Rowan Th 11:15 - 12:30
- Final presentation
- List of approved topics (to be posted before the end of Spring break)
- Topics in Spring 2021:
- Name: Topic
- Nick
"Hammer juggling, rotational
instability, and eigenvalues"
- Available times for rehearsal during the week
of ..... :
- Day-Date:
Times:
- M(onday) -
- T
-
- Rehearsals scheduled: