Large Scale Matrix Computation, Optimization and Learning
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Course Description
Massive data sets are now common to many different fields of research and practice. Classical numerical linear algebra can be prohibitively costly in many modern problems. This course will explore the theory and practice of randomized matrix computation and optimization for large-scale problems to address challenges in modern massive data sets. Applications in machine learning, statistics, signal processing and data mining will be surveyed. Prerequisites: familiarity with linear algebra (ENGR 108 or equivalent), basic probability and statistics (EE 178 or equivalent), basic programming skills.
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
3
Course Repeatable for Degree Credit?
No
Course Component
Lecture
Enrollment Optional?
No
Does this course satisfy the University Language Requirement?
No
Programs
EE270
is a
completion requirement
for:
- (from the following course set: )
- (from the following course set: )