This course overviews the field of computer engineering, the computer engineering curriculum at RIT, and research and career opportunities. The topics covered include basic circuit analysis, number systems, digital logic, programming, robotics, laboratory equipment, teamwork, critical thinking, technical writing, modern and contemporary issues, ethics, diversity, and communication skills.

This course introduces computational problem solving. Basic problem-solving techniques and algorithm development through the process of top-down stepwise refinement and functional decomposition are introduced throughout the course. Classical numerical problems encountered in science and engineering are used to demonstrate the development of algorithms and their implementations. May not be taken for credit by Computer Science, Software Engineering, or Computer Engineering majors. This course is designed for Electrical Engineering and Micro-Electronic Engineering majors and students interested in the Electrical Engineering minor.

This course serves as an introduction to computational thinking using a problem-centered approach. Specific topics covered include: expression of algorithms in pseudo code and a programming language; functional and imperative programming techniques; control structures; problem solving using recursion; basic searching and sorting; elementary data structures such as lists, trees, and graphs; and correctness, testing and debugging. Assignments (both in class and for homework) requiring a pseudo code solution and an implementation are an integral part of the course. An end-of-term project is also required.

Students will gain experience and a better understanding of the application of technologies discussed in classes by working in the field of computing security. Students will be evaluated by their employer. If a transfer student, they must have completed one term in residence at RIT and be carrying a full academic load.

In the first part, the course covers the design of digital systems using a hardware description language. In the second part, it covers the design of large digital systems using the computer design methodology, and culminates with the design of a reduced instruction set central processing unit, associated memory and input/output peripherals. The course focuses on the design, capture, simulation, and verification of major hardware components such as: the datapath, the control unit, the central processing unit, the system memory, and the I/O modules. The lab sessions enforce and complement the concepts and design principles exposed in the lecture through the use of CAD tools and emulation in a commercial FPGA. This course assumes a background in C programming.

This is an introductory course in digital MOS circuit analysis and design. The course covers the following topics: (1) MOSFET I-V behavior in aggressively scaled devices; (2) Static and dynamic characteristics of NMOS and CMOS inverters; (3) Combinational and sequential logic networks using CMOS technology; (4) Dynamic CMOS logic networks, including precharge-evaluate, domino and transmission gate circuits; (5) Special topics, including static and dynamic MOS memory, and interconnect RLC behavior.

The purpose of this course is to expose students to both the hardware and the software components of a digital embedded system. It focuses on the boundary between hardware and software operations. The elements of microcomputer architecture are presented, including a detailed discussion of the memory, input-output, the central processing unit (CPU) and the busses over which they communicate. C and assembly language level programming concepts are introduced, with an emphasis on the manipulation of microcomputer system elements through software means. Efficient methods for designing and developing C and assembly language programs are presented. Concepts of program controlled input and output are studied in detail and reinforced with extensive hands-on lab exercises involving both software and hardware, hands-on experience.

The courses will introduce Artificial Intelligence and Machine Learning topics with practical examples of data, tools, and algorithms. In addition to C, C++, and Matlab, a scripting language (i.e. Python) will be used and taught throughout the course. The course will explore basic artificial intelligence techniques and their applications to engineering problems. Students will be introduced to the following AI foundations: probability and linear algebra, state spaces, algorithms, data processing, feature extraction, feature reduction, classification, and decision making. Some of the techniques and tools to be covered in this course are inference, regression, linear discriminant analysis, decision trees, neural networks, deep learning platforms and architectures, and reinforcement learning. Students are expected to have any of the following programming skills: C/C++, Matlab, Java, or any other high level programming language.

One semester of paid work experience in electrical engineering.

In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters.

A second course that delves further into computational problem solving, now with a focus on an object-oriented perspective. There is a continued emphasis on basic software design, testing & verification, and incremental development. Key topics include theoretical abstractions such as classes, objects, encapsulation, inheritance, interfaces, polymorphism, software design comprising multiple classes with UML, data structures (e.g. lists, trees, sets, maps, and graphs), exception/error handling, I/O including files and networking, concurrency, and graphical user interfaces. Additional topics include basic software design principles (coupling, cohesion, information expert, open-closed principle, etc.), test driven development, design patterns, data integrity, and data security.

Students perform paid, professional work related to their program of study. Students work full-time during the term they are registered for co-op. Students must complete a student co-op work report for each term they are registered; students also are evaluated each term by their employer. A satisfactory grade is given for co-op when both a completed student co-op report and a corresponding employer report that indicates satisfactory student performance are received.