Your program plan will differ depending on previous credit received, your course schedule, and available offerings. It is not recommended to take these courses in combination. For students considering graduating in less than four years, it's important to acknowledge the reasons to undertake such a plan of study. All things considered, please see the tables for three and three and a half year degree options. This requirement is listed in the freshman year curriculum, but many of the options would not be appropriate for a first year student. Complete this requirement in the semester when it is most appropriate to do so i.
Your ESS or faculty adviser can help guide your selection on this requirement. Students must complete a minimum of 20 units of upper division EECS courses. Students must complete one course about engineering ethics or social implications of technology. Students must complete a minimum of 45 units of engineering coursework. The 45 units of engineering courses cannot include:.
Terms offered: Spring This course and its follow-on course EECS16B focus on the fundamentals of designing modern information devices and systems that interface with the real world. Together, this course sequence provides a comprehensive foundation for core EECS topics in signal processing, learning, control, and circuit design while introducing key linear-algebraic concepts motivated by application contexts. The courses are aimed at entering students as well as non-majors seeking a broad foundation for the field. Summer: 8 weeks - 6 hours of lecture, 4 hours of discussion, and 6 hours of laboratory per week.
The course sequence provides a comprehensive introduction to core EECS topics in machine learning, circuit design, control, and signal processing while developing key linear-algebraic concepts motivated by application contexts. Modeling is emphasized in a way that deepens mathematical maturity , and in both labs and homework, students will engage computationally, physically, and visually with the concepts being introduced in addition to traditional paper exercises.
The courses are aimed at entering students as well as non-majors seeking a broad introduction to the field. The course focuses on the fundamentals of designing modern information devices and systems that interface with the real world and provides a comprehensive foundation for core EECS topics in signal processing, learning, control, and circuit design. Logic, infinity, and induction; applications include undecidability and stable marriage problem.
Past papers and marking instructions
Modular arithmetic and GCDs; applications include primality testing and cryptography. Polynomials; examples include error correcting codes and interpolation. Probability including sample spaces, independence, random variables, law of large numbers; examples include load balancing, existence arguments, Bayesian inference. Completion of work in Computer Science Read Less [-].
Slides – Computer Structure
Terms offered: Fall , Fall , Fall An introduction to the kinematics, dynamics, and control of robot manipulators, robotic vision, and sensing. The course covers forward and inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics, and control. It presents elementary principles on proximity, tactile, and force sensing, vision sensors, camera calibration, stereo construction, and motion detection. The course concludes with current applications of robotics in active perception, medical robotics, and other areas.
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This course will cover dynamics and control of groups of robotic manipulators coordinating with each other and interacting with the environment. Concepts will include an introduction to grasping and the constrained manipulation, contacts and force control for interaction with the environment. We will also cover active perception guided manipulation, as well as the manipulation of non-rigid objects. Throughout, we will emphasize design and human-robot interactions, and applications to applications in manufacturing, service robotics, tele-surgery, and locomotion.
Robotic Manipulation and Interaction: Read Less [-]. Terms offered: Spring , Fall , Spring This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Random variables.
Topics (Part III) - The Cambridge Handbook of Computing Education Research
Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection.
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Markov chains. Probability and Random Processes: Read Less [-]. Terms offered: Spring , Fall , Spring This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization. Optimization Models in Engineering: Read Less [-]. Terms offered: Fall , Fall , Fall This course introduces students to the basics of modeling, analysis, and design of embedded, cyber-physical systems.
Students learn how to integrate computation with physical processes to meet a desired specification. Topics include models of computation, control, analysis and verification, interfacing with the physical world, real-time behaviors, mapping to platforms, and distributed embedded systems. The course has a strong laboratory component , with emphasis on a semester-long sequence of projects. Course Objectives: To develop the skills to realize embedded systems that are safe, reliable, and efficient in their use of resources.
To learn how to model and design the joint dynamics of software, networks, and physical processes. To learn to think critically about technologies that are available for achieving such joint dynamics. Introduction to Embedded Systems: Read Less [-]. Terms offered: Spring , Fall , Spring An introduction to digital and system design. The material provides a top-down view of the principles, components, and methodologies for large scale digital system design. The underlying CMOS devices and manufacturing technologies are introduced, but quickly abstracted to higher-levels to focus the class on design of larger digital modules for both FPGAs field programmable gate arrays and ASICs application specific integrated circuits.
The class includes extensive use of industrial grade design automation and verification tools for assignments, labs and projects.
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Students must enroll in at least one of the labs concurrently with the class. Course Objectives: The Verilog hardware description language is introduced and used. Parallelism, pipelining and other micro-architectural optimizations are introduced. A number of physical design issues visible at the architecture level are covered as well, such as interconnects, power, and reliability. Students wishing to take a second lab flavor next term can sign-up only for that Lab section and receive a Letter grade.
Terms offered: Spring , Fall , Spring This lab lays the foundation of modern digital design by first presenting the scripting and hardware description language base for specification of digital systems and interactions with tool flows. The labs are centered on a large design with the focus on rapid design space exploration.
The lab exercises culminate with a project design, e. The design is mapped to simulation and layout specification. Course Objectives: Software testing of digital designs is covered leading to a set of exercises that cover the design flow. Digital synthesis, floor-planning, placement and routing are covered, as well as tools to evaluate timing and power consumption. A series of lab exercises provide the background and practice of digital design using a modern FPGA design tool flow. Digital synthesis, partitioning, placement, routing, and simulation tools for FPGAs are covered in detail.
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