Global Arc

Communication systems have become a ubiquitous part of modern life. This course introduces students to the fundamental of digital communication and wireless systems. Topics include concepts from information, compression, channel, modulation, radio propagation to principles of wireless cellular, and WiFi systems. At the end of the semester, students are expected to gain a deep understanding of the basis of wireless communication systems and the connection between theoretical concepts and real-world systems.

Introduces distributed algorithms to optimize networked systems in electronic, mechanical, or biochemical substrates and other methodologies of optimization, both structures and numerical algorithms, for a variety of engineering applications. Applications will be selectively drawn from the following: computer networking, Internet protocols, communication systems, signal processing, circuit design, controlled dynamic systems, computational geometry, and financial engineering. Two 90-minute lectures. Prerequisite: MAT 202. No previous exposure to optimization theory, algorithms, or any specific application areas is required.

This course will introduce the matrix form of quantum mechanics and discuss the concepts underlying the theory of quantum information. Some of the important algorithms will be discussed, as well as physical systems which have been suggested for quantum computing. Prerequisite: Linear algebra at the level of MAT 202, 204, 217, or the equivalent.

Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical and computer engineering. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty member.

Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical and computer engineering. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty member.

The course covers basic theories of modern machine learning: 1. statistical learning theory: generalization, uniform convergence, Rademacher complexity, VC theory, reproducing Hilbert kernel space and their applications on simple classification/regression models; 2. optimization theory: gradient descent, stochastic gradient descent and their convergence analyses for convex functions, nonconvex functions 3. deep learning theory: basic approximation, optimization and generalization results for deep neural networks; 4. reinforcement learning theory: MDP, Bellman equations, planning, and sample complexity results for value iteration/Q-learning.

This course is an introduction to the theoretical foundations of machine learning. A variety of classical and recent results in machine learning and statistical analysis are discussed, including: classification, regression, regularization, optimization, gradient descent, neural networks, convolutional networks, and reinforcement learning.

An introduction to the properties of solids. Theory of free electrons--classical and quantum. Crystal structure and methods of determination. Electron energy levels in a crystal: weak potential and tight-binding limits. Classification of solids--metals, semiconductors, and insulators. Types of bonding and cohesion in crystals. Lattice dynamics, phonon spectra, and thermal properties of harmonic crystals. Prerequisite: 342, or PHY 208 and 305, or permission of instructor.

Electronic structure of solids. Electron dynamics and transport. Semiconductors and impurity states. Surfaces and interfaces. Dielectric properties of insulators. Electron-electron, electron-phonon, and phonon-phonon interactions. Anharmonic effects in crystals. Magnetism. Superconductivity. Alloys. Three hours of lectures. Prerequisites: 441 or equivalent.

The physics and technology of solid-state electronic devices. Covers electronic structure of semiconductors, energy bands and doping, followed by discussion of carrier transport by drift and diffusion and recombination/generation. Detailed analysis of p-n junctions, bipolar transistors and field effect transistors. Survey of a wide range of devices, including photodetectors, solar cells, light-emitting diodes and semiconductor lasers, highlighting contemporary concepts such as thin film electronics and 2D semiconductors.