24 courses with the subject EECS E, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
EECS E4321DIGITAL VLSI CIRCUITS3.00
Prerequisites: Recommended preparation: ELEN E3106, ELEN E3331, and CSEE W3827. Design and analysis of high speed logic and memory. Digital CMOS and BiCMOS device modeling. Integrated circuit fabrication and layout. Interconnect and parasitic elements. Static and dynamic techniques. Worst-case design. Heat removal and I/O. Yield and circuit reliability. Logic gates, pass logic, latches, PLAs, ROMs, RAMs, receivers, drivers, repeaters, sense amplifiers
EECS E4750Heterogeneous Computing for Signal and Data Processing3.00
Prerequisites: (ELEN E3801) and (COMS W3134) or ELEN E3801 AND COMS W3134 Methods for deploying signal and data processing algorithms on contemporary general purpose graphics processing units (GPGPUs) and heterogeneous computing infrastructures. Using programming languages such as OpenCL and CUDA for computational speedup in audio, image and video processing and computational data analysis. Significant design project
EECS E4764Artificial Intelligence of Things (AIoT)3.00
Prerequisites: Knowledge of programming or instructor's permission. Suggested preparation: ELEN E4703, CSEE W4119, CSEE W4840, or related courses. Artificial Intelligence of Things (AIoT), Internet of Things (IoT), and Cyber-Physical Systems (CPS). Embedded and mobile platforms. Embedded programming. Sensors, actuators, and interfaces. Wireless networking, web services, and databases. Edge and cloud computing. Large language models (LLMs) for AIoT. Time-series data visualization and analytics. Group projects to build end-to-end AIoT systems and applications
Prerequisites: Instructor's permission. Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699
Prerequisites: Instructor's permission. Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699
Prerequisites: Instructor's permission. Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699
Prerequisites: Instructor's permission. Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699
Prerequisites: Instructor's permission. Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699
Prerequisites: Basic calculus, linear algebra, probability, and programming. Basic statistics and machine learning strongly recommended. Bayesian approaches to machine learning. Topics include mixed-membership models, latent factor models, Bayesian nonparametric methods, probit classification, hidden Markov models, Gaussian mixture models, model learning with mean-field variational inference, scalable inference for Big Data. Applications include image processing, topic modeling, collaborative filtering and recommendation systems
Prerequisites: Machine Learning, Linear Algebra, Probability and statistics. EECS E4040 Neural Networks and Deep Learning (or similar) E4620 - Numerical Methods for Data Science (or similar)
Prerequisites: Machine Learning, Linear Algebra, Probability and statistics, EECS E4040 Neural Networks and Deep Learning (or similar) E4620 - Numerical Methods for Data Science (or similar) Working knowledge of Python and a DL SW framework such as TensorFlow or PyTorch
Lect.: 2. Advanced topics spanning Electrical Engineering and Computer Science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899. Spring 2026: EECS E6890 Course Number Section/Call Number Times/Location Instructor Points Enrollment EECS 6890 001/11742 Th 4:10pm - 6:40pm 337 Seeley W. Mudd Building Zhaoyou Wang 3 11/30
Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899
Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899
Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899. Topic: Big Data Analytics
Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899
Advanced topics spanning Electrical Engineering and Computer Science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899. Topic: Advanced Big Data Analytics
Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899. Topic: Quantum Computing and Communication
Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899
Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899
EECS E9601Seminar in Data-Driven Analysis and Computation3.00
points. Lect.: 2. with the instructor's permission. Advanced topics and recent developments in mathematical techniques and computational tools for data science and engineering problems.
