Courses I've taught at Marquette

EECE 113 Linear Systems Analysis

Mathematical descriptions of continuous-time signals and systems are introduced. The time-domain viewpoint is developed for linear time invariant systems using the impulse response and convolution integral. The frequency domain viewpoint is also explored through the Fourier Series and Fourier Transform. Basic filtering concepts including simple design problems are covered. Application of the Laplace transform to block diagrams, linear feedback, and stability including bode plots are discussed. The sampling theorem, the Z-transform, and the Discrete Fourier Transform are introduced.

EECE 157 Digital Signal Processing

This course is an introduction to discrete-time signals and systems. Topics include sampling theory and linear time invariant system analysis through convolution, Fourier transforms and z-transforms. In addition, techniques for the design of digital filters are introduced, and the computation and use of the discrete Fourier transform and fast Fourier transform is discussed. Applications of these concepts is accomplished through several Matlab-based design projects.

EECE 211 Algorithm Analysis and Applications

This course is an introduction to the analysis of algorithms. Topics to be covered include asymptotic complexity notation, recursion analysis, advanced data structures, sorting methodologies, dynamic programming, graph algorithms, and an introduction to several advanced topics such as NP-completeness theory and linear programming.
Course Project: Use dynamic programming algorithm to generate optimal blackjack strategy table for arbitrary card distribution. Try it!

EECE 212 Pattern Recognition

This course is an introduction to the theory and application of statistical pattern recognition, hypothesis testing, and parameter estimation. Topics include probability distribution models, Bayesian decision theory and hypothesis testing, classical and modern approaches to parameter estimation, parametric and non-parametric classifiers. Also covered are diagonalization and the Karhunen-Loeve transform (a.k.a. Principal Components analysis), supervised and unsupervised clustering, Expectation Maximization algorithms for Maximum Likelihood estimation, and linear discriminant analysis.

EECE 222 Optimal and Adaptive Digital Signal Processing

This course is an introduction to optimal and adaptive signal processing techniques, including spectral estimation, Wiener filters, linear prediction, steepest descent and least mean square algorithms, least squares and recursive least squares estimation, and Kalman filters.

EECE 223 Digital Processing of Speech Signals

This course is an introduction to the fundamentals of speech processing, including speech production models and feature analysis, with applications in speech coding, synthesis, and recognition.

COEN 171 Computer Hardware

This course is an overview of computer hardware systems, with emphasis on microprocessor design. Topics include performance analysis, MIPS assembly language, arithmetic logic units, datapath and control aspects of instruction set architectures, pipelining, and memory and I/O devices.