USC is the home of the Signal and Image Processing Institute (SIPI), a leader for the past 40 years in research in the theory and applications of signal and image processing. With 15 tenured and tenure-track faculty and a large number of research faculty, postdocs, technical staff and graduate students, SIPI is among the largest such organizations in the US. Research in SIPI spans theoretical work, which includes compressed sensing, computational imaging, graph-based signal processing, machine learning and fuzzy inference, through applications that include speech processing, brain-computer interfaces, biomedical imaging, multimedia data analysis and human-centered signal processing. We offer a large number of classes in all aspects of signal processing, ranging from the core topics that define the field to specialized courses on the most recent developments in theory and applications. The great majority of our classes are taught by tenured/tenure-track faculty.

MS students following a signal and image processing emphasis should plan a course of study based on the signal and image processing flow chart. At the start of each academic year, information sessions led by SIPI faculty offer incoming students advice on classes and study plans and answer questions. All new students are strongly encouraged to attend.

We strongly recommend that all new students consider taking the four core classes:

Core CoursesUnits
EE 441Applied Linear Algebra for Engineering3
CSCI 455xIntroduction to Programming Systems Design4
EE 483Introduction to Digital Signal Processing3
EE 503Probability for Electrical and Computer Engineers4

 

Each of these classes is designed as a solid introduction to the topic with the goal of preparing students for more advanced classes that will use the skills gained in these classes. The majority of students in these classes are in a MS or PhD program. In our experience, even students who have taken apparently similar classes as undergraduates find these classes rewarding and excellent preparation for more advanced classes.

We offer a broad range of advanced classes in signal and image processing and students are able to choose a number of different areas. We offer the following possible areas as a guide in preparing your plan of study. Note that these groupings are offered as a guideline, not all classes need to be selected from a single area, and they are not degree programs. They are all part of the MSEE general program.

Audio and Speech Processing and Analysis CoursesUnits
EE 519Speech Recognition and Processing for Multimedia3
EE 522Immersive Audio Signal Processing3
EE 559Mathematical Pattern Recognition3
EE 586LAdvanced DSP Design Laboratory4
EE 619Advanced Topics in Automated Speech Recognition3
Image and Video Processing and Analysis CoursesUnits
EE 559Mathematical Pattern Recognition3
EE 566Optical Information Processing3
EE 569Introduction to Digital Image Processing3
EE 574Computer Vision3
EE 586LAdvanced DSP Design Laboratory4
EE 592Computational Methods for Inverse Problems3
EE 596Wavelets3
EE 669Multimedia Data Compression3
Biomedical Imaging and Signal Processing CoursesUnits
EE 523Advanced Biomedical Imaging3
EE 563Estimation Theory3
EE 591Magnetic Resonance Imaging and Reconstruction3
EE 592Computational Methods for Inverse Problems3
General Signal ProcessingUnits
EE 500Neural and Fuzzy Systems3
EE 512Stochastic Processes3
EE 517Statistics for Engineers3
EE 559Mathematical Pattern Recognition3
EE 562Random Processes in Engineering4
EE 563Estimation Theory3
CSCI 567Machine Learning4
CSCI 570Analysis of Algorithms4
EE 583Statistical Signal Processing3
EE 586LAdvanced DSP Design Laboratory4
EE 592Computational Methods for Inverse Problems3
EE 660Machine Learning from Signals: Foundations and Methods3
AI and Machine Learning (see also Data Science and Engineering)Units
EE 500Neural and Fuzzy Systems3
EE 517Statistics for Engineers3
EE 559Mathematical Pattern Recognition3
CSCI 567Machine Learning4
CSCI 570Analysis of Algorithms4
EE 660Machine Learning from Signals: Foundations and Methods3

 

Curriculum Flowcharts