MGT 102: Leadership and Communications

Students learn how to communicate clearly and persuasively, in a way that inspires action. They learn how to tailor communications to different audiences, apply the principles of logical reasoning in structuring communications, connect authentically with their audience through their unique leadership style, and create compelling, high-impact presentations and communications. Classes are often spent on hands-on exercises, and offer ample opportunity for discussion and feedback.

MATH110: Linear Algebra

Linear Algebra is a fundamental subject on systems of linear equations and matrix theory. The course will cover the knowledge and applications of vectors, vector spaces, systems of linear equations, matrices, determinants, linear transformations, inner products, eigenvalues and eigenvectors, etc. that will be useful in other disciplines; especially in Economics and Computer Science.

MATH201: Calculus II

Topics that will be covered in this second semester of introductory calculus are improper integrals, introduction to probability and distributions, infinite series and sequences, Taylor polynomials, Fourier series, vectors and vector functions, partial differentiation, Lagrange multipliers, and topics in differential calculus.

MATH202: Calculus III

Topics that will be covered in this third semester of introductory calculus are multiple integrals in different coordinates; vector calculus, line and surface integrals, Green and Stokes theorems.

MATH203: Ordinary and Partial Differential Equations

Differential equations are the language in which the laws of nature are expressed. Understanding properties of solutions to differential equations is fundamental for much of contemporary science and engineering. Ordinary differential equations (ODEs) deal with functions of one variable, which can be thought of as time. On the second part of the class, three main types of partial differential equations (PDEs) are considered: diffusion equations, elliptic equations, and hyperbolic equations.

PHYS101: Introductory Mechanics

This course introduces the fundamentals of classical physics with a focus on the classical mechanics and its applications to real problems in science and engineering. Topics include: physics and measurements; vectors; motions in one and two dimensions; the Newton’s laws of motions; work and energy; momentum and collisions; rotational motion and gravity; motion of solids and fluids.

PHYS110: Introductory Electricity and Magnetism

This course introduces the fundamentals of classical physics with a focus on the electricity and magnetism. Topics include: electric charge; electric fields; Gauss law, potential; capacitance; electrical current; resistance; circuit; magnetic forces and fields; Ampere’s law; Faraday’s law; Maxwell’s equations; electromagnetics waves.

CHEM101: Core Concepts in Chemistry

Emphasizes core concepts required for organic chemistry, including atomic and molecular structure, chemical equilibrium with application to acids and bases, thermodynamics, chemical kinetics, and reaction mechanisms. Relevance and integrated nature of these concepts illustrated through applications to biological, materials, or environmental chemistry. Laboratory illustrates experimental applications of these concepts.

STA206: Probability and Statistics in Engineering

Topics that will be covered in this course are: computation of probabilities of occurrences in well-defined probability spaces; modeling events of random phenomena using discrete/continuous random variables; sampling techniques; approximation of sampling distributions of the sample mean using the central limit theorem; parameter estimations using point/interval estimators; statistical hypothesis testing, and linear regression analysis.

STA301: Bayesian satistics

STA302: Probability and Stochastic Processes

The objectives of this course is to provide a thorough but straightforward background of basic probability theory; introduce basic ideas and tools of the theory of stochastic processes; and develop and analyze probability models to recognize the randomness in the system probabilistically. The topics are to cover: basic ideas of probability theory; conditional probability and conditional expectation; Markov chains in discrete time; the Poisson process; Markov Processes in continuous time; brief introduction to Brownian motion.

CPS201: Computational Methods in Engineering

Introduction to the scientific computing and numerical methods using Matlab. Students will study simple engineering problems and learn the fundamental numerical techniques that are used to solve them using a computer. Topics include: mathematical modeling; Matlab fundamentals; programming with Matlab; round off and truncation errors; root finding methods; optimization; matrix analysis; linear algebra in Matlab environment; eigenvalues and eigenvectors.

CS111/EE200: Introduction to Computer Science

Introduction to the practices and principles of computer science and programming and their impact on and potential to change the world. algorithmic, problem-solving, and programming techniques using high-level languages (Python) and design techniques emphasizing abstraction, encapsulation, problem decomposition. Topics also consists of Intro to object oriented programing.

CS201: Data Structure and Algorithms

Analysis, use, and design of data structures and algorithms using an object-oriented language like Java to solve computational problems. Emphasis on abstraction including interfaces and abstract data types for lists, trees, sets, tables/maps, and graphs & its algorithms. Methods for external storage such indexing are also introduced.

CS202: Discrete Mathematics for CS

Introduction to the practices and principles of discrete mathematics that deal with discrete objects. Discrete Math is needed to see mathematical structures in the object you work with, and understand their properties. This ability is important for software engineers, data scientists, security and financial analysts. Discrete Math's topics consist of Mathematical Logic, Sets, Relations, Number Theory and Cryptography, Induction and Recursion, Counting, Boolean
Algebra, and Modeling Computation. Prerequisite for all other courses in CS.

CS203: Computer Organization

This course provides you with a basic understanding of how computers work. Starting from basic number and data representation we explore how computer store manipulate information to perform computation. This is followed by higher-level systems designs including memory and input/output.

CS204: Design & Analysis of Algorithms

This course is a study of algorithm design, algorithm complexity analysis, and problem complexity analysis. Design techniques include brute-force, decrease-, divide-, and transform-and-conquer, dynamic programming, greedy algorithms, iterative improvement, backtracking, and branch-and-bound. These techniques can be considered general problem solving tools, whose applications are not limited to traditional computing and mathematical problems. Two factors make this point particularly important. First, more and more computing applications go beyond the traditional domain, and there are reasons to believe that this trend will strengthen in the future. Second, developing students' problem solving skills has come to be recognized as a major goal of college education. Among all the courses in a computer science curriculum, a course on the design and analysis of algorithms is uniquely suitable for this task because it can offer a student specific strategies for solving problems. The course is organized around some fundamental strategies of algorithm design and algorithm design will be taught on a par with analysis. Some more abstract but very important topics will be also included: NP-completeness, approximation algorithms, and lower-bound limits.

CS311: Introduction to Database

This course is intended to give students a solid background in database systems. Topics include data modeling, database design theory, data definition and manipulation languages (i.e., SQL), and indexing techniques, query processing and optimization, and database programming interfaces. Besides relational databases and semi- structured (i.e., XML and JSON), this course also samples a number of other topics related to data management, such data warehousing. Programming projects are required.

CS330: Introduction to artificial intelligence

CS331: Introduction to Data Mining

CS332: Introduction to Machine Learning

CS333: Introduction to Computer Vision

CS411: Big Data & Cloud Computing

CS412: Information Retrieval and Web Search

CS413: Data Preprocessing/cleansing

CS414: Data pipeline/project

CS432: Advanced Data Mining

CS433: Applied Machine Learning

CS435: Practical Deep learning in Natural Language Processing

CS436: Practical Deep learning in Computer Vision

CS437: Pattern Recognition

CS441: Data Visualization

CS442: Practical statistical learning

CS450: Data science topics

CS380/EE391: Undergraduate Research/Independent Study I

Students will work with an academic supervisor to study some undergraduate research topics of their choices. Written report or paper are usually expected by the end of the course.

CS480/EE491: Undergraduate Research/Independent Study II

For seniors only. Students will work with an academic supervisor to study some undergraduate research topics of their choices. Written report or paper are usually expected by the end of the course.

EE201: Fundamentals of Electrical Engineering

The course starts with the introduction about information-bearing electrical signals and systems; and then discuss about the creation, manipulation, transmission, and reception of those electrical signals. Some main topics include elementary signal theory, time- and frequency-domain analysis, sampling theorem, digital information theory, digital transmission of analog signals and error-correcting codes.

EE202: Introduction to Microelectronic Devices and Circuits

Hands-on, laboratory driven introduction to microelectronic devices, sensors, and integrated circuits. Student teams of 3-4 students/team compete in a design, assembly, testing, characterization and simulation of an electronic system. Projects include microelectronic devices, sensors, and basic analog and digital circuits. Classroom portion designed to answer/explain questions generated in laboratory about understanding operation of devices and sensors, and the performance of electronic circuits. Student evaluation based on project specification, prototyping, integration, testing, simulation and documentation.

EE203: Introduction to Digital Systems

Design and implementation of combinational and sequential digital systems with special attention to digital computers. The use of computer-aided design tools, hardware description languages, and programmable logic chips to facilitate larger and higher performance designs will be stressed.

EE204: Introduction to Electromagnetic Fields

Fundamentals and application of transmission lines and electromagnetic fields and waves, antennas, field sensing, and signal transmission. Transmission line transients and digital signal transmission; transmission lines in sinusoidal steady state, impedance transformation, and impedance matching; electrostatics and magneto statics, including capacitance and inductance; electromagnetic waves in uniform media and their interaction with interfaces; antennas and antenna arrays.

EE205: Introduction to Signals and Systems

The course will cover fundamentals of analog and digital signal processing. Similarities and differences of the two classes of signals and systems will be discussed in depth. More particularly, the course would provide knowledge about continuous and discrete signal representation and classification; system classification and response; transfer functions, Fourier series; Fourier, Laplace, and z transforms.

EE206: Laboratory I – Fundamentals

The first goal of this lab is to familiarize students with basic lab safety, circuit component and especially how to use essential instruments such as oscilloscopes, function generator and multi-meter. More importantly, it also allows students to actually visualize the principles and applications of important DC/AC circuits they have learnt theoretically. Each lab is designed with ready-to-use circuit hardware modules, which means that student can have in-depth practice on a variety of fundamental and useful DC/AC circuits/concepts without the need of spending tremendous amount of lab time
on circuit wiring.

EE207: Laboratory II – Microelectronic Devices and Circuits

The students will examine the characteristics of analog devices such as diode, zener diode, transistor and Op-Amp IC in ready-to-use circuit hardware modules. They will also have hand-on experiment with the Design of circuits including rectifier, transistor amplifier and Op-Amp. By the end of the course, the student will have the practical skills that are needed for implementing projects in electronics or repairing electronic equipment.

EE208: Laboratory III – Digital Systems

Laboratory exercises and group design projects will reinforce the various design techniques discussed in EE203.

EE330: Intro to Computer Architecture

Computer structure, assembly language, instruction execution, addressing techniques, and digital representation of data. Computer system organization, logic design, microprogramming, cache and memory systems, and input/output interfaces.

EE331/CS205: Intro to Operating Systems

Basic concepts and principles of multi programmed operating systems. Processes, interprocess communication, CPU scheduling, mutual exclusion, deadlocks, memory management, I/O devices, file systems, protection mechanisms.

EE408: Introduction to Optimization

The course covers basic theories and methods for solving optimization problems, iterative techniques for unconstrained minimization as well as linear and nonlinear programming with engineering applications.

EE410: Digital Signal Processing

The first part of the course provides background (or a review) on the analysis and representation of discrete-time signal systems, including discrete-time convolution, difference equations, the z-transform, and the discrete-time Fourier transform. The second part of the course focuses on the implementation of recursive (infinite impulse response IIR) and non-recursive (finite impulse response FIR) digital filters. Their design to meet given requirements/specifications is also a main focus of the course.

EE411: Embedded DSP Laboratory

By including structured labs and an open-ended design group project, the course will equip students with skills to implement and analyze real-time digital signal processing systems. Knowledge of digital signal processing theory and techniques will be consolidated and deepened via real-world observations and applications. Practical skills including the design and implementation of an open-ended real-time DSP system, teamwork, written and oral presentation are all emphasized/sharpened via a group final project assignment.

EE413: Introduction to Image and Video Processing

The courses will reveal fundamental ‘behind-the-scene’ techniques in image and video processing. It will start with the basics of gray-scale image formation, compression, enhancement as well as segmentation. The techniques then are extended for color images and video ones. The later part of the course will introduce the concepts and application of more advanced image and video processing techniques such as techniques to remove undesired objects from images/videos or to apply sparse modeling and compressing sensing in image analysis and processing in medical applications.

EE414: Digital Audio and Acoustic Signal Processing

This course is designed to provide an introduction to the fundamental concepts, theory, and practice of digital audio and acoustic signal processing (DAASP). Digital audio concerns the process of transducing, digitizing, filtering, transforming, coding, storing, manipulating, transmitting, distributing, analyzing, and reproducing high quality music and other acoustic signals. With the advent of multimedia applications, digital audio signal processing has emerged as a field quite distinct from digital speech processing. The field is extremely broad spanning the disciplines of acoustics, hearing, signal processing, music, and psychophysics. This course will focus on those elements of the field with the greatest signal processing and acoustics content. The emphasis will be on providing students with an intuitive understanding of the principles behind DAASP algorithms. In addition, some experience with the most common algorithms will be provided via MATLAB exercises with real digital audio signals.

EE415: Digital Image and Multidimensional Signal Processing

The course will introduce students to the theory and methods of digital image and video sampling, denoising, coding, reconstruction, and analysis. Both linear methods (such as 2- and 3-D Fourier analysis) and non-linear methods (such as wavelet analysis) will be discussed. Key topics include segmentation, interpolation, registration, noise removal, edge enhancement, halftoning and inverse halftoning, deblurring, tomographic reconstruction, superresolution, compression, and feature extraction.

EE421: Wireless Communication Systems

The purpose of this course is to introduce the principles behind the design of modern wireless communication systems to undergraduate students. The course covers the following topics: radio propagation characteristics and wireless channel models, transmission and reception strategies for wireless channels, multiple access techniques, and radio resource management.

EE424: Introduction to Information and Coding Theory

This course provides an introduction to mathematical measures of information and their connections to practical problems in communication, compression, and inference. Fundamental quantities, such as entropy, mutual information, channel capacity, rate- distortion function, Fisher information, and minimum mean-square estimation error will be defined and their inter-relations will be discussed.

EE425: Graphs & Networks

A mathematical examination of graphs and their application in the sciences. Families of graphs include social networks, small-world graphs, Internet graphs, planar graphs, well- shaped meshes, power-law graphs, and classic random graphs. Phenomena include connectivity, clustering, communication, ranking and iterative processes.

EE426: Linear Control Systems

Analysis and design of feedback control systems. Block diagram and signal flow graph system models. Servomechanism characteristics, steady-state errors, sensitivity to parameter variations and disturbance signals. Time domain performance specifications. Stability. Root locus, Nyquist, and Bode analysis; design of compensation circuits; closed loop frequency response determination. Introduction to time domain analysis and design.

EE427: Introduction to Robotics and Automation

Fundamental notions in robotics, basic configurations of manipulator arm design, coordinate transformations, control functions, and robot programming. Applications of artificial intelligence, machine vision, force/torque, touch and other sensory subsystems. Design for automatic assembly concepts, tools, and techniques. Application of automated and robotic assembly costs, benefits, and economic justification. Selected laboratory and programming assignments.

EE430: Computer Network Architecture

This course will introduce students to the fundamentals of computer networks. The layered architecture of the network protocol stack will be the focus of discussion. A variety of case studies will be drawn from the Internet, combined with practical programming exercises. At the end of the semester, students will well understand several concepts (including the Internet architecture, HTTP, DNS, P2P, Sockets, TCP/IP, BGP, Routing protocols, and wireless/mobile networking) and use them to answer questions such as how to achieve reliable/secure communications over unreliable/insecure channels, how to find a good path through a network, how to share network resources among competing entities, how to find an object in the network, and how to build network applications.

EE431: Advanced Computer Architecture

This course covers topics on advanced computer architecture, and is appropriate for both advanced undergraduates and graduate students. Building on introductory classes which showed how a basic computer functions, this course examines techniques for improving computer performance and usability. Topics covered include virtual memory, pipelining, caches (memory hierarchies), and advanced storage systems.

EE432: Introduction to Embedded Systems

An introduction to hardware/software codesign of embedded computer systems. Structured programming techniques for high and low level programs. Hardware interfacing strategies for sensors, actuators, and displays. Detailed study of Motorola 68HC11 and 68HC12 microcomputers as applied to embedded system development. Hardware and simulation laboratory exercises with 68HC11 and 68HC12 development boards. Major design project.

EE433: Computer Networks

Networking and distributed systems. Network infrastructure support for distributed applications ranging from email to web browsing to electronic commerce. Principles underlying the design of our network infrastructure and the challenges that lie ahead. The socket API, security, naming network file systems, wireless networks, Internet routing, link layer protocols (such as Ethernet), and transport protocols (TCP). Hands-on programming assignments covering issues in distributed systems and networking.

EE434: Fault-Tolerant and Testable Computer Systems

To provide students with an understanding of fault tolerant computers, including both the theory of how to design and evaluate them and the practical knowledge of real fault tolerant systems. The main themes of this course are: technological reasons for faults, fault models, information redundancy, spatial redundancy, backward and forward error recovery, fault-tolerant hardware and software, modeling and analysis, testing, and design for test.

EE435: Performance and Reliability of Computer Networks

Methods for performance and reliability analysis of local area networks as well as wide area networks. Probabilistic analysis using Markov models, stochastic Petri nets, queuing networks, and hierarchical models. Statistical analysis of measured data and optimization of network structures.

EE436: Computer Networks and Distributed Systems

This course provides a research survey of network architecture and protocols. Broadly speaking, we will survey a handful of "classical" research ideas and approaches. We will also explore the state of the art in select networking technologies, protocols and algorithms. We will delve a lot deeper into software defined networking, content distribution techniques, mobile networks, and infrastructures for supporting and delivering online services such as Facebook, Google, and Bing. In each of those protocols the course will place a particular emphasis on the implications of network management (troubleshooting), network security, traffic engineering, and differences between data centers and backbone networks implementations.

EE437: Synthesis & Verification of VLSI Systems

Algorithms and CAD tools for VLSI synthesis and design verification, logic synthesis, multi-level logic optimization, high-level synthesis, logic simulation, timing analysis, formal verification.

EE438: VLSI System Testing

This course will examine in depth the theory and practice of fault analysis, test generation, and design for testability for digital VLSI circuits and systems. Testing tools and systematic design-for-test (DFT) methodologies are necessary to handle design complexity, ensure reliable operation, and achieve short time-to-market. The topics to be covered in the course include: fault modeling; fault simulation; test generation algorithms; testability measures; design for testability and scan design; built-in self-test, delay testing; wafer-level burn-in and test; memory testing; system-on-a-chip test; test compression. Grading will be based on homework assignments, two in-class exams, and a term project, which may be either a research survey, testing of a chip from a previous class or research project that has been fabricated using MOSIS, or a software implementation of a test methodology. Students will get a chance to use commercial DFT tools such as EncounterTest from cadence, Fastscan from Mentor Graphics, and Tetramax from Synopsys.

EE439: CMOS VLSI Design Methodologies

Emphasis on full-custom chip design. Extensive use of CAD tools for IC design, simulation, and layout verification. Techniques for designing high-speed, low-power, and easily-testable circuits. Semester design project: Groups of four students design and simulate a simple custom IC using Mentor Graphics CAD tools. Teams and project scope are multidisciplinary; each team includes students with interests in several of the following areas: analog design, digital design, computer science, computer engineering, signal processing, biomedical engineering, electronics, photonics. A formal project proposal, a written project report, and a formal project presentation are also required. The chip design incorporates considerations such as cost, economic viability,
environmental impact, ethical issues, manufacturability, and social and political impact.

EE440: Fundamentals of Microelectronic Devices

Fundamentals of semiconductor physics and modeling (semiconductor doping technology, carrier concentrations, carrier transport by drift and diffusion, temperature effects, semiconductor device models). Principles of semiconductor device analysis (current-voltage and capacitance-voltage characteristics). Static and dynamic operation of semiconductor contacts, PN junction diodes, MOS capacitors, MOS field-effect transistors (MOSFETs), and bipolar-junction transistors (BJTs). SPICE models and parameter extraction.

EE441: Introduction to Electronics: Integrated Circuits

Analysis and design of electronic circuits in bipolar and MOS technologies, with emphasis on both large-signal and small-signal methods. Circuits for logic gates, latches, and memories. Single-stage and multistage amplifiers and op amps. Circuits with feedback, including stability and frequency response considerations. Analog and mixed analog/digital circuit applications. Extensive use of SPICE for circuit simulation.

EE442: Semiconductor Devices for Integrated Circuits

Basic semiconductor properties (energy-band structure, effective density of states, effective masses, carrier statistics, and carrier concentrations). Electron and hole behavior in semiconductors (generation, recombination, drift, diffusion, tunneling, and basic semiconductor equations). Current-voltage, capacitance-voltage, and static and dynamic models of PN Junctions, Schottky barriers, Metal/Semiconductor Contacts, Bipolar-Junction Transistors, MOS Capacitors, MOS-Gated Diodes, and MOS Field- Effect Transistors. SPICE models and model parameters.

EE443: Analog Integrated Circuits

Analysis and design of bipolar and CMOS analog integrated circuits. SPICE device models and circuit macromodels. Classical operational amplifier structures, current feedback amplifiers, and building blocks for analog signal processing, including operational transconductance amplifiers and current conveyors. Biasing issues, gain and bandwidth, compensation, and noise. Influence of technology and device structure on circuit performance. Extensive use of industry-standard CAD tools, such as Analog Workbench.

EE444: Integrated Circuit Engineering

Basic processing techniques and layout technology for integrated circuits. Photolithography, diffusion, oxidation, ion implantation, and metallization. Design, fabrication, and testing of integrated circuits.

EE445: Digital Integrated Circuits

Analysis and design of digital integrated circuits. IC technology. Switching characteristics and power consumption in MOS devices, bipolar devices, and
interconnects. Analysis of digital circuits implemented in NMOS, CMOS, TTL, ECL, and BiCMOS. Propagation delay modeling. Analysis of logic (inverters, gates) and memory (SRAM, DRAM) circuits. Influence of technology and device structure on performance and reliability of digital ICs. SPICE modeling.

EE446: Analog Integrated Circuit Design

Design and layout of CMOS analog integrated circuits. Qualitative review of the theory of pn junctions, bipolar and MOS devices, and large and small signal models. Emphasis on MOS technology. Continuous time operational amplifiers. Frequency response, stability and compensation. Complex analog subsystems including phase-locked loops, A/D and D/A converters, switched capacitor simulation, layout, extraction, verification, and MATLAB modeling. Projects make extensive use of full custom VLSI CAD software.

EE447: CAD for Mixed-Signal Circuits

The course focuses on various aspects of design automation for mixed-signal circuits. Circuit simulation methods including graph-based circuit representation, automated derivation and solving of nodal equations, and DC analysis, test automation approaches including test equipment, test generation, fault simulation, and built-in-self-test, and automated circuit synthesis including architecture generation, circuit synthesis, tack generation, placement and routing are the major topics.