Covers foundations of estimation theory and machine learning in a probabilistic modeling framework. Topics include frequentist and Bayesian estimation, analysis of estimators, linear regression, linear classification, graphical models, Markov models, sampling methods, and variational inference. Requires APMA 3100 or an equivalent course on Probability, familiarity with linear algebra, and Python programming.

Studies the modeling, analysis, design, computer simulation, and measurement of electrical circuits which contain non-linear devices such as junction diodes, bipolar junction transistors, and field effect transistors. Includes the gain and frequency response of linear amplifiers, power supplies, and other practical electronic circuits. This course is taught in the studio mode with mixed lecture and lab. Prerequisite: ECE 2630, APMA 2130 co-requisite

Analyzes the basic laws of electromagnetic theory, beginning with static electric and magnetic fields, and concluding with dynamic E&M fields; plane wave propagation in various media; Maxwell's Laws in differential and integral form; electrical properties of matter; transmission lines, waveguides, and elementary antennas. Prerequisite: APMA 2130, ECE 2300, and ECE 2200 or equivalent.

Digital CMOS circuit design and analysis: combinational circuits, sequential circuits, and memory. Second order circuit issues. Global design issues: clocking and interconnect. Use of Cadence CAD tools. Team design of a significant VLSI chip including layout and implementation. Prerequisites: ECE 2330 and (ECE 2660 or ECE 2600)

Digital CMOS circuit design and analysis: combinational circuits, sequential circuits, and memory. Second order circuit issues. Global design issues: clocking and interconnect. Use of Cadence CAD tools. Semester long team research project investigating new areas in circuit design. Prerequisites: ECE 2630, ECE 2330.

Student-led special topic courses which vary by semester.

Learn about and experiment with machine learning algorithms using Python. Applications include image classification, removing noise from images, and linear regression. Students will collect and interpret data, learn machine learning theory, build systems-level thinking skills required to strategize how to break the problem down into various functions, and to implement, test and document those functions. Prerequisite: CS 111X

A second-level undergraduate course covering a topic not normally covered in the course offerings. The topic usually reflects new developments in the electrical and computer engineering field. Offering is based on student and faculty interests.

A second-level undergraduate course covering a topic not normally covered in the course offerings. The topic usually reflects new developments in the electrical and computer engineering field. Offering is based on student and faculty interests.

Develops tools for analyzing signals and systems in continuous and discrete-time, for controls, communications, signal processing and machine learning. Primary concepts are the representation of signals and linear systems in the time domain (convolution, differential equations, state-space representation) and in the frequency domain (Fourier/Laplace analysis) including practical programming examples. Co-requisite: APMA 2130 or MATH 3250, and Prerequisite: (ECE 2300 or ECE 2501 Topic: Applied Circuits)