2023 Fall
MATH 156 001 - LEC 001
Numerical Analysis for Data Science and Statistics
Jon A Wilkening, Zachary James McNulty
Class #:32826
Units: 4
Instruction Mode:
In-Person Instruction
Offered through
Mathematics
Current Enrollment
Total Open Seats:
20
Enrolled: 25
Waitlisted: 0
Capacity: 45
Waitlist Max: 5
No Reserved Seats
Hours & Workload
3 hours of instructor presentation of course materials per week, and 9 hours of outside work hours per week.
Final Exam
FRI, DECEMBER 15TH
08:00 am - 11:00 am
Etcheverry 3109
Other classes by Zachary James McNulty
Course Catalog Description
Introduction to applied linear algebra, numerical analysis and optimization with applications in data science and statistics.
Topics covered include:
• 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
Rules & Requirements
Repeat Rules
Course is not repeatable for credit.
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