
General information 
Instructor:
Malgorzata Peszynska, Professor of Mathematics
(Contact information including office hours on
instructor's department website)
Class:
Lecture: MWF 11:0011:50pm, STAG 212.
Course information: Credits: 3.00.
Student preparation: MTH 256 and MTH 341 are required. An
ability to write at a level appropriate for 4/5XX class and an ability
to (learn to) use MATLAB will be expected. Students struggling with
math, writing, or MATLAB skills must communicate with the instructor
immediately in week one to discuss a plan of action, and will be
expected to catch up promptly.
Class announcement.
Textbook, assignments and resources: class notes with ample
references will be posted in CANVAS.
Syllabus: The class covers various discrete and continuous
mathematical models along with the mathematical methods required to
analyze and solve them. The methods include linear analysis,
equilibrium and minimum principles, calculus of variations, principal
component analysis (singular value decomposition) and orthogonal
expansions, Fourier analysis, least squares, and constrained and
unconstrained optimization. As time permits, a gentle introduction to
inverse problems, machine learning, and stochastic techniques will be
included.
Grade for the class will be based on the Homework grade grade
(50%), Exam grade (40%) from two exams worth 20% each, and class
participation (10%). No late HW will be accepted but one lowest HW
grade will be dropped.
Exams: (F 4/19 or W 4/24) and F 5/17 in class. There will be no
makeup exams.
Class participation: Attendance in lectures is not taken but
students are responsible for the material in course notes and for the
material covered in class. Class participation grade will be assigned
based on class activities and student presentations of solutions to
the exercises posted in course notes. Daily schedule will be posted on
class website as a guide, but the instructor may schedule adhoc
activities as needed.
Course Learning Outcomes:
A successful student who has completed
MTH 420 will be able to
 Follow the mathematical modeling steps for selected applications
which translate a given problem to one that can be solved using
algebra and differential equations.
 Solve discrete and continuous quadratic minimization problems
arising from physically motivated equilibrium problems and calculus of
variations.
 Apply the basics of Fourier analysis to selected examples.
 Use the principles of principal component analysis and least
squares for solving, in particular, large underdetermined and
overdetermined linear systems arising, e.g., from data science.
A successful student who has completed MTH 520 will be able to:
 Follow the mathematical modeling steps for selected applications
which translate a given problem to one that can be solved using
algebra and differential equations.
 Formulate and solve discrete and continuous quadratic
minimization problems arising from physically motivated equilibrium
problems and calculus of variations.
 Apply Fourier analysis in discrete and continuous setting and
understand its limitations.
 Use the principles of principal component analysis and least
squares for solving, in particular, large underdetermined and
overdetermined linear systems arising, e.g., from data science. Select
the most appropriate method for a given application.
Statement Regarding Students with Disabilities: Accommodations
for students with disabilities are determined and approved by
Disability Access Services (DAS). If you, as a student, believe you
are eligible for accommodations but have not obtained approval please
contact DAS immediately at 5417374098 or at
http://ds.oregonstate.edu.
DAS notifies students and faculty members of approved
academic accommodations and coordinates implementation of those
accommodations. While not required, students and faculty members are
encouraged to discuss details of the implementation of individual
accommodations.
Student Conduct Expectations link:
http://studentlife.oregonstate.edu/code

