Undergraduate Programs



  • ENGR-E 101 Innovation and Design (3 cr.) Innovation and Design provides an introduction to Intelligent Systems Engineering. Students learn about engineering and the focus areas through interactive lectures and hands-on activity quests. Students present each quest with a new media to practice presenting data. Students will learn about professional development and start a digital portfolio.  
  • ENGR-E 110 Engineering Computing Architectures (3 cr.) This course introduces the architecture of computing systems from logic gates through arithmetic logic units, central processing unit, and memory. It proceeds through the integration into a simple, but complete computing device including the necessary software elements.  
  • ENGR-E 111 Software Systems Engineering (4 cr.) This course covers core aspects of the practice of software engineering, from basic programming concepts to design to development, debugging and maintenance. This course will cover software design, considering abstraction, modularity and encapsulation. It will cover requirements and process management, testing and maintenance, common software structures and software development tools.  Credit given for only one of ENGR-E 111, CSCI-C 212, H 212 or A 592.
  • ENGR-E 201 Computer Systems Engineering (3 cr.) P: ENGR-E 110. This course covers modern computing devices, the computing ecosysytem and introductory material in systems programming, computer architecture, operating systems and computer networks.  Coursework includes fundamental concepts at the basis of modern computing systems, covering costs in time, space and energy.  The curriculum includes basic operational concepts in programming, computer architecture and networking.
  • ENGR-E 210 Engineering Cyber-Physical Systems (3 cr.) P: ENGR-E 201 and E 221. This course provides an introduction to core topics in cyber-physical systems. These topics include embedded systems, issues of real-time processing, and sensor mechanisms and control algorithms. Students will study applications of these elements in the Internet of Things and Robotics.
  • ENGR-E 221 Intelligent Systems I (3 cr.) P: MATH-M 211, M 212, PHYS-P 221 and ENGR-E 111. This course introduces important concepts about intelligent systems.  It provides a basis in mathematical tools and algorithms used in AI and machine learning. It introduces optimization techniques used in Intelligent Systems II.  It will describe many current examples and how they are implemented in cloud systems.  The course is based on Python for data analytics.
  • ENGR-E 222 Intelligent Systems II (3 cr.) P: ENGR-E 221. In this course students will be familiarized with different specific applications and implementations of intelligent systems and their use in desktop and cloud solutions.
  • ENGR-E 250 Systems, Signals, and Control (3 cr.) P: MATH-M 211 and M 212. Many engineering systems are based on signal processing and this course covers fundamental concepts in signals, systems, and control theory. Basic topics are covered, including continuous and discrete time signals and systems, filtering and sampling, Fourier transforms and its variants, and basic feedback systems.
  • ENGR-E 299 Engineering Professionalization & Ethics (1 cr.) This course introduces topics in engineering related to professionalism and ethics designed to develop ethical reasoning skills, increase ethical awareness and professionalism, and to analyze ethical dilemmas, specific to engineering. Students will learn ethical principles that can be applied in research, design and development. An eight-week course. 
  • ENGR-E 311 Circuits and Digital Systems (3 cr.) P: ENGR-E 110 and PHYS-P 222. This course will cover elements of circuits, such as the operation of basic circuit elements, fundamental circuit laws, and analytic techniques in both the time domain and the frequency domain. It will also cover the transistor-level design of circuits in the context of modern integrated-circuit technology. Credit given for only one of ENGR-E 111, CSCI-C 212, H 212, or A 592.
  • ENGR-E 314 Embedded Systems (3 cr.) This course covers Embedded and Real-Time Systems designed for real-time multiprocessing and distributed processing. It discusses theoretical and practical concepts in real-time systems emphasizing both hard and soft real-time distributed multi-processing. Several operating systems (e.g. Xinu, Linux, VxWorks), computer architectures and process scheduling methods will be used to illustrate concepts. Credit not given for both ENGR-E 314 and E 514.
  • ENGR-E 315 Digital Design with FPGAs (3 cr.) P: ENGR-E 101 and E 221. This course introduces digital design techniques using field programmable gate arrays (FPGAs). It discusses FPGA architecture, digital design flow using FPGAs, and other technologies associated with field programmable gate arrays. The course study will involve extensive lab projects to give students hands-on experience on designing digital systems on FPGA platforms.
  • ENGR-E 317 High Performance Computing (3 cr.) P: Beginner/intermediate C/C++ experience. Familiarity with Linux/Unix command-line utilities. Students will learn the development, operation, and application of high performance computing systems prepared to address future challenges demanding capability and expertise in HPC. The course is interdisciplinary combining critical elements from hardware technology and architecture, system software and tools, and programming models and application algorithms with the cross-cutting theme of performance management and measurement. Credit not given for both ENGR-E 317 and E 517.
  • ENGR-E 321 Advanced Cyber-Physical Systems (3 cr.) P: ENGR-E 210 or equivalent. This course is the entry point into the cyber-physical systems specialization. It provides in-depth coverage of core topics in cyber-physical systems. It will treat issues of data analysis and reactive actuation, as well as power management and mobility. The course will explore formal models for designing and predicting system behavior.  
  • ENGR-E 327 Automated Fabrication Machines (3 cr.) P: ENGR-E 210. This course will engage students in understanding fabrication machines as cyber-physical systems using computer numeric control (CNC), and in understanding how they work by designing, constructing, and programming such devices. This course will provide hands-on experience developing and using 2D and 3D graphics primitives and implementing devices that provide them.  
  • ENGR-E 332 Introduction to Modeling and Simulation (3 cr.) P: MATH-M 211, M 212, M 343, PHYS-P 221, P 222 or equivalent. This course introduces computational modeling and simulation used for solving problems in many engineering fields. Basics of deterministic and stochastic simulation methods are covered. Optimization techniques, use of high-performance computing, and engineering applications of simulations are discussed.  
  • ENGR-E 340 Introduction to Computational Bioengineering (3 cr.) P: ENGR-E 331 and BIOL-L 112 or equivalent. ENGR-E 332 recommended or may be taken concurrently. This course introduces key computational modeling techniques for bioengineering, with a focus on cell population kinetics, cell signaling, receptor trafficking, pharmacokinetics/pharmacodynamics, and compartmental and systems physiology methods. Concepts in control theory and optimization will also be applied to steer the modeled biological systems towards design objectives.
  • ENGR-E 390 Undergraduate Independent Study (1-3 cr.) P: Instructor's permission. Independent research based on existing literature or original work. A report, in the syle of a departmental technical report, is required. May be repeated for a maximum of 6 credit hours.
  • ENGR-E 399 Topics in Intelligent Systems Engineering (1-3 cr.) P: Must be a student in the ISE undergraduate program or instructor's permission. Variable topic. Emphasis is on new developments and research in Intelligent Systems Engineering.  May be repeated with different topics.
  • ENGR-E 416 Engineering Cloud Computing (3 cr.) P: Experience with Windows or Linux using Java and scripts. The course covers basic concepts on programming models and tools of cloud computing to support data intensive science applications. Students will get to know the latest research topics of cloud platforms, parallel algorithms, storage and high level language for proficiency with a complex ecosystem of tools that span many disciplines.   Credit not given for both ENGR-E 416 and E 516.
  • ENGR-E 434 Big Data Applications (3 cr.) P: Java and/or Python will be used as programming languages. This is an overview course of Big Data Applications covering a broad range of problems and solutions. It covers cloud computing technologies and includes a project. Algorithms are introduced and illustrated.   Credit given for only one of ENGR-E 434, E 534, INFO-I 423, or I 523.
  • ENGR-E 435 Image Processing (3 cr.) P: Experience with signal processing or machine learning; Linear algebra and Calculus II. The input or output of many engineering tools are images. Therefore, engineers need to know how to process them. Image processing will teach students how to design and implement their own algorithms for automatically detecting, classifying, and analyzing objects in images.  
  • ENGR-E 440 Computational Methods for 3-D Biomaterials (3 cr.) P: ENGR-E 331, E 340 and PHYS-P 222 or equivalent or instructor permission. This computational engineering course teaches key biophysics and numerical concepts needed to simulate 3-D biological tissues, including finite element methods, conservation laws, biotransport, fluid mechanics, and tissue mechanics.  The entire course will combine lectures with hands-on lab projects to simulate 3-D biological materials, and prepare students for computational tissue engineering. Credit not given for both ENGR-E 440 and E 540.
  • ENGR-E 441 Simulating Cancer as an Intelligent System (3 cr.) P: Familiarity with advanced engineering mathematics. This course explores cancer as an adaptive intelligent system, where renegade cells break the rules, reuse the body's natural processes to re-engineer their environments and evade treatments. We will use computational models to explore this system and the potential for future clinicians to plan treatments with data-driven models.   Credit not given for both ENGR-E 441 and E 541.
  • ENGR-E 483 Information Visualization (3 cr.) This course provides students with a working knowledge on how to visualize abstract information and hands-on experience in the application of this knowledge to specific domains, different tasks, and diverse, possibly non-technical users.   Credit not given for both ENGR-E 483 and E 583.
  • ENGR-E 484 Scientific Visualization (3 cr.) This course teaches basic principles of human cognition and perception; techniques and algorithms for designing and critiquing scientific visualizations in different domains (neuro, nano, bio-medicine, IoT, smart cities); hands-on experience using modern tools for designing scientific visualizations that provide novel and/or actionable insights; 3D printing and augmented reality deployment; teamwork/project management expertise.   Credit not given for both ENGR-E 484 and E 584.
  • ENGR-E 490 Engineering Capstone Design I (3 cr.) P: Junior or senior standing. Engineering Capstone Design I is one of two capstone requirements for all Intelligent Systems Engineering students. Students will design engineering projects based on their areas of concentration, which will be supported by dedicated faculty members. Students may choose to conduct advanced research, develop prototypes, design new products or redesign existing products.  
  • ENGR-E 491 Engineering Capstone Design II (3 cr.) P: Junior or senior standing. Engineering Capstone Design II is the second of two capstone requirements for all Intelligent Systems Engineering students. Students will design engineering projects based on their areas of concentration, which will be supported by dedicated faculty members. Students may choose to conduct advanced research, develop prototypes, design new products or redesign existing products.  

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