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 Courses | Units | |
|---|---|---|
| EE 510 | Applied Linear Algebra for Engineering | 3 |
| CSCI 455x | Introduction to Programming Systems Design | 4 |
| EE 483 | Introduction to Digital Signal Processing | 3 |
| EE 503 | Probability for Electrical and Computer Engineers | 4 |
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 Courses | Units | |
|---|---|---|
| EE 519 | Speech Recognition and Processing for Multimedia | 3 |
| EE 522 | Immersive Audio Signal Processing | 3 |
| EE 559 | Mathematical Pattern Recognition | 3 |
| EE 586L | Advanced DSP Design Laboratory | 4 |
| EE 619 | Advanced Topics in Automated Speech Recognition | 3 |
| Image and Video Processing and Analysis Courses | Units | |
|---|---|---|
| EE 559 | Mathematical Pattern Recognition | 3 |
| EE 566 | Optical Information Processing | 3 |
| EE 569 | Introduction to Digital Image Processing | 3 |
| EE 574 | Computer Vision | 3 |
| EE 586L | Advanced DSP Design Laboratory | 4 |
| EE 592 | Computational Methods for Inverse Problems | 3 |
| EE 596 | Wavelets | 3 |
| EE 669 | Multimedia Data Compression | 3 |
| Biomedical Imaging and Signal Processing Courses | Units | |
|---|---|---|
| EE 523 | Advanced Biomedical Imaging | 3 |
| EE 563 | Estimation Theory | 3 |
| EE 591 | Magnetic Resonance Imaging and Reconstruction | 3 |
| EE 592 | Computational Methods for Inverse Problems | 3 |
| General Signal Processing | Units | |
|---|---|---|
| EE 500 | Neural and Fuzzy Systems | 3 |
| EE 512 | Stochastic Processes | 3 |
| EE 517 | Statistics for Engineers | 3 |
| EE 559 | Mathematical Pattern Recognition | 3 |
| EE 562 | Random Processes in Engineering | 4 |
| EE 563 | Estimation Theory | 3 |
| CSCI 567 | Machine Learning | 4 |
| CSCI 570 | Analysis of Algorithms | 4 |
| EE 583 | Statistical Signal Processing | 3 |
| EE 586L | Advanced DSP Design Laboratory | 4 |
| EE 592 | Computational Methods for Inverse Problems | 3 |
| EE 660 | Machine Learning from Signals: Foundations and Methods | 3 |
| AI and Machine Learning (see also Data Science and Engineering) | Units | |
|---|---|---|
| EE 500 | Neural and Fuzzy Systems | 3 |
| EE 517 | Statistics for Engineers | 3 |
| EE 559 | Mathematical Pattern Recognition | 3 |
| CSCI 567 | Machine Learning | 4 |
| CSCI 570 | Analysis of Algorithms | 4 |
| EE 660 | Machine Learning from Signals: Foundations and Methods | 3 |