Students will learn about the role of electrical and computer engineers in today's world while developing soft and hard skills to prepare them to be successful in the major and their careers. Through a series of lectures, design projects, and labs, students will explore topics spanning core areas of Electrical and Computer Systems Engineering.

Students will learn about the role of electrical and computer engineers in today's world while developing soft and hard skills to prepare them to be successful in the major and their careers. Through a series of lectures, design projects, and labs, students will explore topics spanning core areas of Electrical and Computer Systems Engineering.

Students will learn about the roles of electrical and computer engineers in today's world while developing soft and hard skills to prepare them to be successful in the major and their careers. Through a series of lectures, design projects, and labs, students will explore topics spanning core areas of Electrical and Computer Systems Engineering.

Students will learn to model circuit elements, circuit models, and apply techniques for circuit analysis. Concepts include analyzing steady state response for inactive and active elements and the transient response of first and second order systems. Course includes a laboratory component.

Students will learn to model circuit elements, circuit models, and apply techniques for circuit analysis. Concepts include analyzing steady state response for inactive and active elements and the transient response of first and second order systems. Course includes a laboratory component.

Experience in design methodology for Electrical and Computer Science Engineering students. Students will learn to critically and holistically evaluate problems involving electronics, computational hardware and software while producing solutions that address societal, economical, and technical needs.

Experience in design methodology for Electrical and Computer Science Engineering Honors-level students. Students will learn to critically and holistically evaluate problems involving electronics, computational hardware, and software while producing solutions that address societal, economical, and technical needs.

Basic principles and design techniques of embedded computing. The student will develop an integrative understanding of components, systems, software, and design. These objectives will be achieved through case studies, exercises, example models, and laboratory exercises. Emphasis will be placed on design experience and teamwork.

Advanced principles and design techniques of embedded computing. The student will develop an integrative understanding of components, systems, software, and design. These objectives will be achieved through case studies, exercises, example models, and laboratory exercises. Emphasis will be placed on design experience and team-work.

A broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning (the use of labeled datasets to train algorithms to classify data or predict), unsupervised learning (uses machine learning algorithms to analyze and cluster unlabeled datasets), learning theory (bias/variance tradeoffs), and practical advice.

Students will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead and implement successful Deep Learning projects. Students will learn about convolutional networks, recurrent neural networks, and long short-term memory. The basics of machine learning will also be covered.

Introduction to the theory and practice of error control codes. Topics include linear, cyclic, BCH, convolutional, and turbo codes. The use of codes in various systems is discussed throughout the course. The course includes the construction and modeling of error control systems in Matlab or similar environment.