The course will cover the application of machine learning in CPU design, covering topics such as feature engineering, perceptrons, neural networks, recurrent neural networks, LSTMs, language models, generative adversarial learning, genetic algorithms, AI Agents, ML interpretation, reinforcement learning and other machine learning concepts for solving design challenges related to performance, power, and security; solving challenges common in many engineering fields. You will also get hands-on experience in training and inference of the latest Large Language Models on custom data. 

This course will introduce fundamental concepts in robot motion planning with a focus on spatial manipulators utilizing simulation and, if available, real robots. The course’s topics will include rigid-body spatial transformations, robot kinematics, trajectory generation, configuration space, and sampling-based path planning. Course projects and exercises will utilize a high-level programming language and modern tools and environments used in robotics.

Introduction to basic principles of radar and key radar sub-systems: transmitter and receiver architectures; sub-components, e.g. filters, amplifiers, mixers and oscillators; sources of degradation, such as noise and non-linearity; radar range equation, phenomenology (target reflectivity models, clutter, stealth and scattering), radar measurements of range and velocity, basic radar waveforms, pulse compression, coherency. Technical writing proficiency and communication skills will be honed in this course through oral and written student presentations/assignments. Hands-on experience will be gained through lab experiments/projects conducted using a software-defined CW/FMCW radar.

This course focuses on applying advanced electromagnetic principles and semiconductor theory to design silicon photonic integrated circuits. Key principles such as matrix optics, waveguide theory, coupled mode theory, and P-I-N junctions will be used to design practical silicon photonic devices which are relevant in today’s foundries. Topics include passive wavelength filters, active switches, high-speed optical modulators, and photodetectors for optical communication and computing systems.

This course covers the fundamentals in algorithm design and analysis, focusing on the themes of efficient algorithms and intractable problems.  Topics include divide and conquer algorithms, graph algorithms, greedy algorithms, dynamic programming, NP-completeness and reductions.  The course goal is to provide a solid foundation in algorithms for students in preparation for a job in industry and more advanced courses.

Introduction to dynamics and control for robotic systems tailored for computer scientists. Concepts including ordinary differential equations, kinematics, and dynamics for common air and ground robotic systems will be introduced. Systems concepts such as step, impulse responses, Laplace Transform will be introduced. Feedback control via classical methods (e.g., Nyquist, Bode), PID, and modern state-space and observer-based design will be explored. Emphasis on implementation, and simulation on an aerial multicopter robot will help students visualize and evaluate learning and control design performance.

• Basic understanding. Recombination processes. Laser diode characteristics. Optical gain and loss. Frequency response. Rate equations. Interaction of photons and electrons/holes.
• Basic engineering calculations. Efficiency and power versus current. Gain versus carrier density in quantum wells, bandgap versus alloy composition. Determining internal parameters (internal loss, efficiency, and gain) from laser diode data.
• Advanced calculations. Scattering and transfer matrixes for cavities. Response versus frequency. Quantum well energy states. Transverse waveguide modes. Threshold carrier densities.
• Design optoelectronic devices. Design of DFB, VCSEL, or DBR laser. Design of quantum well for specific energy transitions. Design of cavity dimensions and mirror losses for minimum threshold current.

Suggested Pre-requisites: Programming experience (an object-oriented language), basic linear algebra (ECE 220, MA 305 or 405), basic vector calculus (MA 242 or equivalent), and basic probability and statistics (ST 370/371 or equivalent).

Techniques for the design of neural networks for machine learning. An introduction to deep learning. Emphasis on theoretical and practical aspects including implementations using state-of-the-art software libraries.

This course provides an introduction to quantum algorithms primarily through inner product space and signal processing perspectives. As such, it will be advantageous for students to be familiar with linear algebra (e.g., Math 305 or 405), linear systems (ECE 301) and signal processing (e.g., ECE 410), and basics of quantum computing, specifically quantum gates. Because most students lack at least part of this background, we will review these materials during the first half of the course. It will also be helpful for students to be familiar with probability and statistics (e.g., ST 371 or ECE 514). Some programming proficiency, for example in Matlab or Python, could be helpful.

This course will introduce fundamental concepts in robot motion planning with a focus on spatial manipulators utilizing simulation and, if available, real robots. The course’s topics will include rigid-body spatial transformations, robot kinematics, trajectory generation, configuration space, and sampling-based path planning. Course projects and exercises will utilize a high-level programming language and modern tools and environments used in robotics.

Suggested Pre-requisites: Interest in microelectronic devices, unconventional electronics, computing, biophysics, neuroscience

Topics include: History of electronics & computing, limitations, elements of neuroscience (ions, cells, neurons, synapses, higher-order phenomena), artificial synapses & neurons, contemporary applications (physical ANNs with memory arrays, on-chip processing & classification, logic & decision making, adaptive & evolvable electronics, sensorimotor learning in robotics, neuromorphic bio-interfaces, biocomputing)

Suggested Pre-requisites: C or better in ECE 200 and ECE 211

Introduction to System Printed Circuit Board designing for microcontroller-based embedded computer systems.

Suggested Pre-requisite: ECE 309 or CSC 316

This course introduces how wearable and mobile systems sensors can be used to gather data relevant to understand health, how the data can be analyzed with advanced signal processing and machine learning, and the evaluation performance of these systems in terms of diagnostics and disease progression detection. The course will also touch on how to solve privacy concerns in building mobile health systems in the real world.

Suggested Pre-requisites: E 304 or ECE 304 or ECE 302 or MSE 355 or PY 401 or PY 407

This course provides an in-depth exploration of the physics and operational principles of various qubit technologies, which are fundamental building blocks of quantum computing. The course is designed to equip students with a comprehensive understanding of different types of qubits, their underlying physics, and the challenges associated with their implementation and scaling.