Overview
We live in an era of unprecedented creation, transmission, and storage of data, ranging from traditional signals such as speech and sensor data to video streams and user preference data. The Signal Processing & Machine Learning track provides students with the tools they need to transform signals and data into information. Building on a strong mathematical foundation, successful graduates develop core knowledge spanning statistical signal processing, classical machine learning methodology, and deep learning.
A graduate degree is usually required in order to obtain an engineering position in signal processing or data science. Our graduate track is designed to provide this knowledge and prepare students for successful careers for both those seeking a job in industry upon graduation and those planning to continue on to graduate programs and research careers.
The signal processing & machine learning track will change starting Fall 2019 and will follow the requirements listed below. These courses may not match up with what is currently listed in the PSU Bulletin. EE 516, 518, and 519 may all be initially scheduled as EE 510 courses but will still fulfill the requirements. If you run into prerequisite errors while registering for any course please contact eceinfo@pdx.edu for an override. If you have any questions about these changes or how it affects your degree requirements, please contact Dr. McNames at mcnames@pdx.edu.
Program Requirements
Students starting the MS program in sensors and signal processing should have a solid background in calculus, linear algebra, probability & statistics, signals & systems, transform analysis, and high-level programming (such as Python or Matlab). Students who have completed ECE 315, ECE 316, and STAT 351 with a B or better should be well prepared to start this track. Please contact the track director James McNames <mcnames@pdx.edu>, if you have questions about how to prepare for this track.
Core Courses
EE 516 Mathematical Foundations of Machine Learning
EE 520 Random Processes
EE 522 Discrete Time Processing
EE 523 Estimation & Detection
Depth and Breadth Courses
EE 513 Introductory Image Processing
EE 514 Advanced Image Processing
EE 515 Computer Vision
EE 518 Machine Learning Theory & Algorithms
EE 519 Deep Learning Theory & Practice
EE 525 Spectral Estimation
EE 526 Adaptive Filters
EE 527 Sensor Array Processing
EE 528 State Space Tracking
EE 529 Practicum
Track Completion Forms
Program Completion Form
Thesis Program Completion Form
Track Director