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)

This lab provides practical exposure and continuation of the topics covered in the lecture sections of ECE 3250. Topics include principles of measurement and analysis using computerized instrumentation. Co-requisite ECE 3250

A third-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.

This course is an introduction to the foundations behind modern data analysis and machine learning. The first part of the course covers selected topics from probability theory and linear algebra that are key components of modern data analysis. Next, we cover multivariate statistical techniques for dimensionality reduction, regression, and classification. Finally, we survey recent topics in machine learning. Prerequisite: CS 2130

The objective of this course is to provide the basic concepts and algorithms required to develop mobile robots that act autonomously in complex environments. The main emphasis is on mobile robot locomotion and kinematics, control, sensing, localization, mapping, path and motion planning. Prerequisites: APMA 2130, APMA 3080, APMA 3100, and CS 2130 or equivalent. 

This class discusses solid state devices that are used for renewable energy application. While we will provide a general overview of most new and interesting technologies via lectures, discussions, and research presentations, we will focus on the detailed technical aspects of few devices namely: solar cells, thermionic devices, thermoelectric devices, solar thermal (CSPs), and batteries.

This course introduces photovoltaics and solar energy generation and gives an overview of the subject. The course will describe the operation of photovoltaic cells and efficiency improvements, industrial processes, solar thermal power generation, thin films, and nanomaterials for photovoltaics and future technologies.  Prerequisites: ECE 2200 or PHYS 2415 and APMA 2130 or MATH 3250.