To determine if a course is remote, hybrid, or in-person use the catalog search tool to narrow results. Otherwise, when browsing courses, the section indicates teaching mode:

R = Remote
H = Hybrid
0 = In-person

Teaching modes (remote, hybrid, in-person) are subject to change at any point. Please pay close attention when registering. Depending on the timing of a teaching mode change, faculty also may be in contact with students.

MATH 307
Computational Linear Algebra Spring 2021
Division III Quantative/Formal Reasoning

Class Details

Linear algebra is of central importance in the quantitative sciences, including application areas such as image and signal processing, data mining, computational finance, structural biology, and much more. When the problems must be solved computationally, approximation, round-off errors, convergence, and efficiency matter, and traditional linear algebra techniques may fail to succeed. We will adopt linear algebra techniques on a large scale, implement them computationally, and apply them to core problems in scientific computing. Topics may include: systems of linear and nonlinear equations; approximation and statistical function estimation; optimization; interpolation; data scraping; singular value decomposition; and more. This course could also be considered a course in numerical analysis or computational science.
The Class: Format: lecture; To afford students flexibility during the COVID pandemic, this course is taught online. Students will read and/or watch lecture material asynchronously and will participate in a once-per-week synchronous small-group tutorial meeting with the instructor via video chat. This course will be a good fit for students with a strong interest in applied mathematics and a willingness to devote significant effort to learning/doing computer programming.
Limit: 20
Expected: 20
Class#: 5353
Grading: yes pass/fail option, yes fifth course option
Requirements/Evaluation: Students will complete checkpoint quizzes, regularly assigned homework problems and projects, and reflective writing assignments. To move towards a non-hierarchical, transparent, and egalitarian grading system, the instructor follows the policy of "ungrading." Over the course of the semester, students will develop a rubric to assess their own learning and will evaluate themselves according to this rubric.
Prerequisites: MATH 250; some prior exposure to computer programming experience is strongly recommended but not required.
Enrollment Preferences: Professor's discretion
Distributions: Division III Quantative/Formal Reasoning
QFR Notes: This course involves developing the formal mathematical language of linear algebra. It also involves using quantitative tools to solve problems relating to a wide range of applications in the physical and social sciences.

Class Grid

Updated 4:16 pm

Course Catalog Search


(searches Title and Course Description only)
TERM




TEACHING MODE
SUBJECT
DIVISION



DISTRIBUTION



ENROLLMENT LIMIT
COURSE TYPE
Start Time
End Time
Day(s)