Vector Space Methods with Applications

3 credits · Graduate

Key concepts from advanced linear algebra that are used regularly in ECE/CS journal papers on signal processing, communications, circuit design, and machine learning (e.g., logic, topology, vector spaces, optimization). Key mathematical ideas/proofs will be presented and applied. Uses application topics such as Markov chains, alternating projections, pattern classification to illustrate important mathematical topics. Background in linear algebra, a high-level programming language, and probability is assumed. Prerequisites: [(Math 216, 221, or 218-2) & (EGR 103L or CS 201) & (STA 130 or STA 240L or Math 230 or Math 340 or ECE 380 or ECE 555 or EGR 238L) & ECE 280L] or graduate student standing.