Spring 2023
MATH 191 002 - SEM 002
Experimental Courses in Mathematics
NUMERICAL ANALYSIS FOR DATA SCIENCE AND STATISTICS
Jon A Wilkening
Class #:33533
Units: 4
Instruction Mode:
In-Person Instruction
Offered through
Mathematics
Current Enrollment
Total Open Seats:
26
Enrolled: 24
Waitlisted: 0
Capacity: 50
Waitlist Max: 10
No Reserved Seats
Hours & Workload
2 to 8 hours of outside work hours per week, and 1 to 4 hours of student-instructor coverage of course materials per week.
Final Exam
TUE, MAY 9TH
11:30 am - 02:30 pm
Evans 60
Evans 4
Other classes by Jon A Wilkening
Course Catalog Description
The topics to be covered and the method of instruction to be used will be announced at the beginning of each semester that such courses are offered. See departmental bulletins.
Class Description
Introduction to applied linear algebra and optimization with applications in data science.
We will cover Parts I,II,III,VI,VII (and a few subtopics of IV,V) of Strang’s book, as well as chapters 2 and 3 of Higham’s book and Chapter 2 of Demmel’s book, including:
• Floating-point arithmetic, condition number, perturbation theory, backward stability analysis
• Matrix decompositions (LU/QR/Cholesky/SVD), least squares problems, orthogonal matrices
• Eigenvalues, eigenvectors, Rayleigh quotients, generalized eigenvalues
• Principal components, low rank approximation, compressed sensing, matrix completion
• Convexity, Newton’s method, Levenberg-Marquardt method, quasi-Newton methods
• Randomized linear algebra, stochastic gradient descent
• Machine learning, neural networks (deep/convolution), adjoint methods, backpropagation
Class Notes
Prerequisites: Math 53 and 54 or equivalent (e.g., Math 91 from Fall 2022 can replace Math 54)
Rules & Requirements
Repeat Rules
Reserved Seats
Current Enrollment
No Reserved Seats
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials
Associated Sections
None