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Electrical and Computer Engineering

Mike Wiegers Department of Electrical and Computer Engineering
3108 Engineering Hall
1701D Platt St.
Manhattan, KS 66506
785-532-5600
Fax: 785-532-1188
ece@k-state.edu

Hours: Monday-Friday
8 a.m.-noon, 1-5p.m.

CPSWin Education

WICOM

Cyber-Physical Systems (CPS) integrate computational and physical processes to interact with humans. The computational  aspect of modern CPS has witnessed explosive growth in recent decades with developments in tools like statistical and mathematical modeling, and advances in machine learning and optimization algorithms. As a result, there is a great demand for trained engineers proficient in carrying out these computational studies. K-State offers a wide range of courses that equip students with the fundamental skill set necessary to design, analyze and deploy modern CPS. CPSWin graduates enter the workforce with sufficient academic background and research experience to make an immediate impact in several fields including smart grid, biomedical and complex systems.

List of Courses

CIS 890 Pattern Recognition and Machine Learning

ECE 660 Communication Systems I

ECE 661 Communication Systems II

ECE 662 Communication Circuit Design

ECE 764 RF Microwave engineering

ECE 840 Computer Engineering Methods for Analysis, Simulation, and Design

ECE 861 Applied Probability Theory and Random Processes

ECE 887 Distribution System Engineering

ECE 890/690 Intro to Wireless Communications

ECE 962 Advanced Topics in Communications: OFDM and MC-CDMA

ECE 963 Signal Detection and Estimation Theory

ECE 965 Information Theory

IMSE 882 Network Flows and Graph Theory

MATH 713 Advanced Applied Matrix Theory

MATH 715 Applied Mathematics I

MATH 716 Applied Mathematics II

MATH 725 Mathematics of Data and Networks

STAT 761 Discrete Optimization and Scalability for Data Science

STAT 768 Applied Bayesian Modeling and Prediction

STAT 770 Theory of Statistics I

STAT 771 Theory of Statistics II

STAT 850 Linear Models I

STAT 851 Linear Models II

STAT 950 Supervised Statistical Learning - Theory and Application