Last updated on October 20, 2009.

EECS 101 - 'CS 101': An Introduction to Computer Science For Everyone

This course is a non-programming introduction to the field of computer science suitable for freshmen, non-majors, and majors who would like to understand the scope of the field of Computer Science, its key intellectual questions, and its impact on the worlds of technology, business, politics, and law. It covers, at a high level, theory and algorithms, systems and networking, programming languages and software engineering, human-computer interaction and graphics, artificial intelligence and machine learning, security, and some current crazy ideas.

This course is a required Core course in the CS curriculum in McCormick and Weinberg

This course satisfies the Weinberg Area III (Social and Behavioral Sciences) Distribution.

EECS 110 - Introduction to Computer Programming

Introduction to programming practice using a modern programming language. Analysis and formulation of problems for computer solution. Systematic design, construction, and testing of programs. Substantial programming assignments. Fall and Winter are offered in C, Spring is offered in Python.

This course is approved for Weinberg Area II (Formal Studies) distribution credit.

EECS 111 - Fundamentals of Computer Programming II

This is an introductory course on the fundamentals of computer programming. I see this class as an opportunity for you, the student, to see what computer programming is all about and (more importantly) to see whether you want to spend the next few years doing more of it. This course will include weekly programming projects, readings, a midterm, and final examinations. Class participation is not optional. This course is approved for Weinberg Area II (Formal Studies) distribution credit.

This course is a required Core course in the CS curriculum in McCormick and Weinberg.

EECS 130 - Tools and Technology of the World Wide Web

Introduction to the theory and practice of developing sites on and technology for the World Wide Web. It will cover the basics of HTML, JavaScript, ASP, and CGI programming.

EECS 202 - Introduction to Electrical Engineering

An introduction to the concepts and applications of electrical engineering. Topics include quantization, binary representation, performance; power spectral density, digital filtering; fundamental limitations; control systems, Feedback systems; properties of lasers; amplifiers, passive circuit elements, active circuit elements; electronic devices and materials.

EECS 203 - Introduction to Computer Engineering

Overview of computer engineering design. Number systems and Boolean algebra. Logic gates. Design of combinational circuits and simplification. Decoders, multiplexers, adders. Sequential logic and flip flops. Introduction to assembly language. Application of concepts to a computer engineering design project.

EECS 205 - Fundamentals of Computer System Software

Basics of assembly language programming. Macros. System stack and procedure calls. Techniques for writing assembly language programs. The features of IA-32 based PC will be used. Interfaces between high-level languages and assembly codes will be discussed.

EECS 211 - Fundamentals of Computer Programming II

Object-oriented programming, classes and data hiding, dynamic object construction and destruction, derived classes and inheritance, virtual functions; functions, call by value/reference, overloading; abstract data types; standard template libraries; exception handling; introduction to UNIX, file processing, process management.

EECS 213 - Introduction to Computer Systems

This course has four purposes: (1) to learn about the hierarchy of abstractions and implementations that comprise a modern computer system; (2) to demystify the machine and the tools that we use to program it; (3) to come up to speed on systems programming in C in the Unix environment; (4) to prepare students for upper-level systems courses.

This course is a required Core course in the CS curriculum in McCormick and Weinberg

EECS 221 - Fundamentals of Circuits

Fundamental concepts in electrical circuits; circuit analysis and network theorems; linearity and superposition; series/parallel combinations of R, L, and C circuits; sinusoidal forcing; complex frequency and Bode plots; mutual inductance and transformers; two port networks.

EECS 222 - Fundamentals of Signals and Systems

Comprehensive introduction to analysis of continuous and discrete-time signals and systems. Linear time-invariant systems, convolution; Fourier series representations of periodic signals; Continuous time and discrete time Fourier transforms; Laplace transform; z-transform.

EECS 223 - Fundamentals of Solid State Engineering

Introduction to Solid State Engineering. Crystalline state of matter. Quantum phenomena, quantum mechanics. Electrons in atoms, atoms in crystals, electrons in crystals. Energy band structures. Semiconductors. Thermal properties of crystals.

EECS 224 - Fundamentals of Electromagnetics and Photonics

Concepts of flux, potential, gradient, divergence, curl, and field intensity. Boundary conditions and solutions to Laplace and Poisson equations. Capacitance and inductance calculations. Conductors, insulators, and magnetic materials. Introduction to Electromagnetic Waves and Transmission Lines.

EECS 225 - Fundamentals of Electronics

Fundamental concepts in electronics. Diode, BJT and FET Circuits; design using ideal operational amplifiers; feedback; frequency response; biasing; current sources and mirrors; small-signal analysis; design of operational amplifiers.

EECS 230 - Programming for Engineers

Introduction to programming, basic data types, basic control structures; object-oriented programming, classes, constructors and destructors, derived class and inheritance, pointers; engineering applications. Not for Computer Engineering majors.

EECS 231 - Advanced Programming for Computer Engineers

Object-oriented programming, classes and data hiding, dynamic object construction and destruction, derived classes and inheritance, virtual functions; file processing; introduction to UNIX; testing and test generation.

EECS 250 - Physical Electronics and Devices

The physical basis of electronic devices and their application in analog and digital systems. Diodes, transistors, LEDs, photodetectors, and lasers are described, and their properties are explored.

EECS 270 - Applications of Electronic Devices

DC and AC networks, rectifiers, transistor amplifiers, feedback and operational amplifiers, digital electronics, and microprocessors. Not open to electrical or computer engineering majors.

EECS 302 - Probabilistic Systems and Random Signals

Basic concepts of probability theory and statistics, random variables, moments; multiple random variables, conditional distributions, correlation; random signals; applications to engineering systems.

EECS 303 - Advanced Digital Logic Design

Overview of digital logic design. Implementation technologies, timing in combinational and sequential circuits, EDA tools, basic arithmetic units, introduction to simulation and synthesis using VHDL.

EECS 307 - Communications Systems

Analysis of analog and digital communications systems, including modulation, transmission, and demodulation of AM, FM, and TV systems. Design issues, channel distortion and loss, bandwidth limitations, additive noise.

EECS 308 - Advanced Electromagnetics and Photonics

Electromagnetic waves, transmission lines; impedance transformation; transients on lines; wave reflection and transmission; metallic waveguides and wave transmission; antenna and diffraction, antenna arrays, communication, and radar.

EECS 310 - Mathematical Foundations of Computer Science

This course will discuss fundamental concepts and tools in discrete mathematics with emphasis on their applications to computer science. Example topics include logic and Boolean circuits; sets, functions, relations, databases, and finite automata; deterministic algorithms and randomized algorithms; analysis techniques based on counting methods and recurrence equations; trees and more general graphs.

This course is a required Core course in the CS curriculum in McCormick and Weinberg

EECS 311 - Data Structures and Data Management

The design, implementation, and analysis of abstract data types, data structures and their algorithms. Topics include: data and procedural abstraction, linked lists, stacks, queues, binary trees, searching, and sorting.

This course is a required Core course in the CS curriculum in McCormick and Weinberg

EECS 313 - Telecommunication Networks for Multimedia

Signals and bandwidth concepts, spectra, basics of electronics, information and coding, modulation, multiplexing, transmission systems, transmission media, analog versus digital communications, computer networks, and switching techniques. Not for electrical engineering or computer engineering majors.

EECS 317 - Data Management and Information Processing

Data models and database design. Modeling the real world: structures, constraints, and operations. The entity relationship to data modeling (including network hierarchical and object-oriented), emphasis on the relational model. Use of existing database systems for the implementation of information systems. Note: This course is only for IE/MFE students - CS/CIS students are not allowed to register for this course, and it does not carry credit towards the CS/CIS major

EECS 322 - Compilers

The compiler is the programmer's primary tool. Understanding the compiler is therefore critical for programmers, even if they never build one. Furthermore, many design techniques that emerged in the context of compilers are useful for a range of other application areas. This course introduces students to the essential elements of building a compiler: parsing, context-sensitive property checking, code linearization, register allocation, etc. To take this course, students are expected to already understand how programming languages behave, to a fairly detailed degree. The material in the course builds on that knowledge via a series of semantics preserving transformations that start with a fairly high-level programming language and culminate in machine code. PREREQUISITE: EECS 395/495 Programming Languages, offered in Winter only

EECS 325 - Artificial Intelligence Programming

Introduction to Lisp and programming knowledge-based systems and interfaces. Strong emphasis on writing maintainable, extensible systems. Topics include: semantic networks, frames, pattern matching, deductive inference rules, case-based reasoning, discrimination trees. Project-driven. Substantial programming assignments.

EECS 328 - Numerical Methods for Engineers

Introduction to numerical methods; numerical differentiation, numerical integration, solution of ordinary and partial differential equations. Students write programs in C++, FORTRAN, C, or Matlab using methods presented in class.

EECS 330 - Human Computer Interaction

Introduction to human-computer interaction and the design of systems that work for people and their organizations. The goal is to understand the manner in which humans interact with, and use, their computers for productive work. The course focus is on the interface as designed artifact. The interface is a design problem without a single "correct" solution but which has many "good" solutions and a plethora of "bad" solutions. Class discussion centers on what makes an interface good and proven techniques for designing interfaces that meet human needs.

EECS 332 - Digital Image Analysis

Introduction to computer and biological vision systems, image formation, edge detection, image segmentation, texture, representation and analysis of two-dimensional geometric structures, and representation and analysis of three-dimensional structures.

EECS 333 - Introduction to Communication Networks

Data communication basics, Telephone, cellular, cable and computer networks, Layered network architectures, models, and protocols, Switching, routing, flow control, and congestion control, Medium access control, ARQ, and local area networks. Queueing models and network performance analysis.

EECS 334 - Introduction to Computer Vision

Please contact the Professor teaching this course for more information

EECS 335 - Introduction to the Theory of Computation

Note: This course replaces Math 374. This course can be used towards fulfilling the CS breadth and requirements in theory. This course gives an introduction to the mathematical foundations of computation. The course will look at Turing machines, universal computation, the Church-Turing thesis, the halting problem and general undecidability, Rice's theorem, the recursion theorem, efficient computation models, time and space (memory) bounds, deterministic and nondeterministic computation and their relationships, the P versus NP problem and hard problems for NP and beyond.

EECS 336 - Design and Analysis of Algorithms

Analysis techniques: solving recurrence equations. Algorithm design techniques: divide and conquer, the greedy method, backtracking, branch-and-bound, and dynamic programming. Sorting and selection algorithms, order statistics, heaps, and priority queues.

EECS 337 - Introduction to Semantic Information Processing

A semantics-oriented introduction to natural language processing, broadly construed. Representation of meaning and knowledge inference in story understanding, script/frame theory, plans and plan recognition, counter-planning, and thematic structures.

EECS 338 - Practicum in Intelligent Information Systems

A practical excursion into the building of Intelligent Information Systems. Each student will develop a working program in the area of information access, management, capture, or retrieval. Project definition, data collection, technology selection, implementation, and project management. Semantics-oriented introduction to natural language processing, broadly construed. Representation of meaning and knowledge inference in story understanding, script/frame theory, plans and plan recognition, counter-planning, and thematic structures.

EECS 339 - Introduction to Databases

Data models and database design. Modeling the real world: structures, constraints, and operations. The entity relationship to data modeling (including network hierarchical and object-oriented), emphasis on the relational model. Use of existing database systems for the implementation of information systems.

EECS 340 - Introduction to Computer Networking

A top-down exploration of networking using the 5-layer model and the TCP/IP stack. HTTP, FTP, DNS, BSD Sockets, concurrent servers, checksums, reliable transport with stop-and-wait, go-back-n, selective repeat, flow control, congestion control, TCP, unicast routing, multicast routing, router architecture, IP, IPv6, IP multicast, MAC protocols and LANs, Ethernet,wireless networks, and network security. Over the course of the quarter, students build web clients and servers, a fully compatible TCP/IP stack that can run them, and evaluate routing protocols in simulation.

EECS 343 - Operating Systems

A fundamental overview of operating systems. Topics covered include: Operating system structures, processes, process synchronization, deadlocks, CPU scheduling, memory management, file systems, secondary storage management. Requires substantial programming projects.

EECS 344 - Design of Computer Problem Solvers

Principles and practice of organizing and building AI reasoning systems. Topics include pattern-directed rule systems, truth-maintenance systems, and constraint languages.

EECS 345 - Distributed Systems

Basic principles behind distributed systems (collections of independent components that appear to users as a single coherent system) and main paradigms used to organize them.

EECS 346 - Microprocessor System Design

Structure and timing of typical microprocessors. Sample microprocessor families. Memories, UARTS, timer/counters, serial devices and related devices. MUX and related control structures for building systems. Interrupt programming. Hardware/software design tradeoffs.

EECS 347 - Microprocessor System Projects

Design, prototype and test individual projects involving microprocessors and programmable logic devices. Introduction to programmable logic devices such as PAL, FPGA, etc. Introduction to and discussion of other circuits as needed for the selected projects.

EECS 348 - Introduction to Artificial Intelligence

Core techniques and applications of artificial intelligence. Representation retrieving and application of knowledge for problem solving. Hypothesis exploration, theorem proving, vision and neural networks.

EECS 349 - Machine Learning

Machine Learning is the study of algorithms that improve automatically through experience. Topics covered typically include Bayesian Learning, Decision Trees, Genetic Algorithms, Neural Networks.

EECS 350 - Introduction to Computer Security

The past decade has seen an explosion in the concern for the security of information. This course introduces students to the basic principles and practices of computer and information security. Focus will be on the software, operating system and network security techniques with detailed analysis of real-world examples. Topics include cryptography, authentication, software and operating system security (e.g., buffer overflow), Internet vulnerability (DoS attacks, viruses/worms, etc.), intrusion detection systems, firewalls, VPN, Web and wireless security. Students with good performance in the class will be awarded researchship in the academic year and/or the summer. This course can help satisfy the project course requirement for undergraduates and satisfy the breadth requirement in computer systems for system Ph.D. students.

EECS 351 - Introduction to Computer Graphics

First in a 3-course series to teach fundamental ideas underlying all forms of computer-assisted picture-making. Teaches you to write simple programs that make any interactive 2D/3D shapes with simple lighting and textured surfaces. You will learn by doing, with 4 projects that teach 1) 2-D and 3-D viewing transformations, 2) polygonal shape descriptions, 3) lighting and shading 4) textures and surfaces using OpenGL (DirectX is similar). Surveys advanced topics covered in later courses, including ray tracing, global illumination, particle systems, implicit surfaces GPUs and more.

EECS 352 - Machine Perception of Music & Audio

Machine extraction of musical structure in audio, MIDI and score files, covering areas such as source separation and perceptual mapping of audio to machine-quantifiable measures.

EECS 353 - Digital Microelectronics

Logic families, comparators, A/D and D/A converters, combinational systems, sequential systems, solid-state memory, large-scale integrated circuits, and design of electronic systems.

EECS 354 - Network Penetration and Security

This RTFM course will focus on remote computer penetration (hacking). The class will introduce basic theory for many different types of attacks and then actually carry them out in ’real-world’ settings. The goal is to learn security by learning how to view your machine as a ’hacker.’ In addition, we will be preparing for the 2007 International Capture the Flag Competition (held each December by UCSB). Capture the Flag is a network security exercise where the goal is to exploit other machines while defending your own.

EECS 355 - ASIC and FPGA Design

Overview of Computer Aided Design tool flow for ASIC and FPGA Design. Synthesis from hardware description languages and creation of finite state machines. Differences between FPGA and ASIC design flows. Exploration of concepts in several projects.

EECS 356 - Introduction to Formal Specification and Verification

Introduction to formal techniques used for system specifications and verifications: temporal logic, set theory, proofs, and model checking. TLA+ (Temporal Logic of Actions) specifications. Safety and liveness properties. Real time specs and verifications.

EECS 357 - Introduction to VLSI CAD

Basic concepts in VLSI CAD with emphasis on physical design, fundamental algorithms for CAD problems, development of CAD tools.

EECS 358 - Introduction to Parallel Computing

Introduction to parallel computing for scientists and engineers. Shared memory parallel architectures and programming, distributed memory, message-passing data-parallel architectures, and programming.

EECS 359 - Digital Signal Processing

Discrete-time signals and systems, Discrete-Time Fourier Transform, z-Transform, Discrete Fourier Transform, Digital Filters.

EECS 360 - Introduction to Feedback Systems

Linear feedback control systems, their physical behavior, dynamical analysis, and stability. Laplace transform, frequency spectrum, and root locus methods. System design and compensation using PID and lead-lag controllers. Digital implementations of analog controllers.

EECS 361 - Computer Architecture I

Design and understanding of the computer system as a whole unit. Performance Evaluation and its role in computer system design; Instruction Set Architecture design, Datapath design and optimizations (e.g., ALU); Control design; Single cycle, multiple cycle and pipeline implementations of processor; Hazard detection and forwarding; memory hierarchy design; Cache memories, Virtual memory, peripheral devices and I/O.

EECS 362 - Computer Architecture Project

Quarter long team project that entails designing a processor for a complete Instruction Set. Involves ISA design, design of components, datapath and control for a pipelined processor to implement the ISA. The design is performed using industry strength design tools and VHDL is used as the design specification language. The design is evaluated using benchmark programs for correctness and performance.

EECS 363 - Digital Filtering

Recursive and nonrecursive digital filters, decimation and interpolation, A/D and D/A conversion as digital filtering problems. Implementation of nonrecursive filters via FFT, quantization problems, e.g., companding and limit cycles.

EECS 366 - Designing and Constructing Models with Multi-agent Languages

Please contact the Professor teaching this course for more information

EECS 370 - Computer Game Design

Fundamentals of computer game design. Topics include: Plot, narrative and character, simulation for creating game worlds, AI for synthetic characters, tuning game play. Substantial programming and project work.

EECS 372 - Designing and Constructing Models with Multi-Agent Languages

This course focuses on the exploration, construction and analysis of multi-agent models. Sample models from a variety of content domains are explored and analyzed. Spatial and network topologies are introduced. The prominent agent-based frameworks are covered as well as methodology for replicating, verifying and validating agent-based models. We use state of the art ABM and complexity science tools. This course can help satisfy the project course and artificial intelligence area course requirement for CS and CIS majors, and satisfy the breadth requirement in artificial intelligence for Ph.D. students in CS. It also satisfies a design course requirement for Learning Sciences graduate students, counts towards the Cognitive Science specialization and as an advanced elective for the Cognitive Science major.

EECS 374 - Introduction to Digital Control

Discrete dynamics systems; discrete models of continuous systems feedback and digital controllers; analog-digital conversion; digital control design including PID, lead-lag, deadbeat, and model matching controllers.

EECS 378 - Digital Communications

Sampling and time-division multiplexing, baseband digital signals and systems. Coded pulse modulation, error control coding, digital modulation systems, information measure and source encoding, and introduction to spread spectrum communications.

EECS 379 - Introduction to Lasers and Fiber Optics

Optical fields as a subset of electromagnetic fields, optical cavities, theory of laser action, and the basics of optical waveguides, including optical fiber.

EECS 380 - Wireless Communications

Overview of existing and emerging wireless communications systems; interference, blocking, and spectral efficiency; radio propagation and fading models; performance of digital modulation in the presence of fading; diversity techniques; Code-Division Multiple Access.

EECS 381 - Electronic Properties of Materials

Quantum physics; electrons and energy bands in crystals; electronic transport in materials, superconductivity; optical properties of materials and their applications; magnetic properties of materials and their applications; thermal properties of materials.

EECS 382 - Photonic Information Processing

Introduction to photonic information processing; coherent and incoherent light; electro-optic and acousto-optic modulation; optical signal processing; holography; optical storage.

EECS 383 - Fiber-Optic Communications

Introduction to fiber-optic communications. Semiconductor diode lasers, internal modulation, electro-optic modulation, coherent and incoherent detection, optical fibers and their properties, optical amplifiers, communication systems, and optical networks.

EECS 384 - Solid State Electronic Devices

Applications of energy band models for semiconductors. Carrier statistics and transport. Diodes, bipolar and field-effect transistors. Integrated circuits. Heterojunction devices.

EECS 385 - Optoelectronics

Introduction to solid-state optoelectronic devices; display devices, laser diodes, photodetectors, and light modulators; optical waveguides and fibers; system application of optoelectronic devices.

EECS 386 - Computational Electromagnetics and Photonics

Introduction to the finite-difference time-domain (FDTD) method in numerical modeling of electromagnetic and optical wave interactions with engineering structures. Topics: finite differences; Maxwell's equations; numerical dispersion and stability; free-space and waveguide field sources; absorbing boundary conditions; material dispersions and nonlinearities.

EECS 388 - Nanotechnology

Physics and technology of nanoscale photonic and electronic devices. Bulk crystal, thin film and epitaxial growth technologies. Semiconductor characterization techniques. Defects in crystals. Nanotechnology processing: diffusion oxidation, ion implantation, annealing, etching, and photolithography. Nanoscale optoelectronic and electronic devices.

EECS 389 - Superconductivity and Its Applications

Properties of materials in the superconducting state; charge flow dynamics of type II superconductors; high Tc superconductors; applications for computers and high-frequency devices.

EECS 390 - Introduction to Robotics

Homogeneous vector and plane, homogeneous transformation, position and orientation transformations, kinematics and inverse kinematic solutions of robot manipulators, Jacobian and inverse Jacobian relation, robot trajectory and task planning, dynamic formulation and computation of robot manipulators, robot programming and control systems.

EECS 391 - VLSI Systems Design

Design of CMOS digital integrated circuits, concentrating on architectural and topological issues. Tradeoffs in custom design, standard cells, gate arrays. Use of VLSI design tools on a small project.

EECS 392 - VLSI Systems Design Projects

Design of a cutting-edge VLSI chip. Teams of 5 to 10 students undertake a large circuit design problem, going from specification to VLSI implementation while optimizing for speed, area, and/or power. Group collaboration and engineering design.

EECS 393 - Design and Analysis of High-Speed Integrated Circuits

Issues that arise in the design and analysis of VLSI circuits at high speeds such as buffer sizing, repeater insertion, noise, electromigration, Elmore delay, scaling trends, and power consumption. Cross-listed as EECS 493.

EECS 394 - Software Project Management

EECS 394 is focused on the process of software development from the perspective of both rapid prototyping and responsive relationships with clients. We'll take an Extreme Programming approach in which teams will maintain tight, iterative development cycles that include ongoing interactions with clients. In the style of EDC, the class is project oriented, with teams focused on specific projects during the entire 10 week period. The class also has a studio feel, with team interactions during class guided by faculty and graduate students.

EECS 395 - Please contact professor for course description

Please contact professor for course description

EECS 395 - Algorithmic and Economic Aspects of Social Networks

Algorithmic and economic fundamentals of social networks, including standard measures of network analysis like clustering, diameter, and degree distribution; network clustering and community detection algorithms; random and strategic network formation models; search, diffusion, and learning in networks.

EECS 395 - Programming Languages Seminar

This is a course where students learn to read research papers in programming languages. The particular topic of papers changes from offering to offering. Contact Prof.Findler for details on a specific offering.

EECS 395 - Computational Photography

Computational photography combines plentiful low-cost computing, digital sensors, actuators, and lights to escape the limitations of traditional film-like methods. New methods offer unbounded dynamic range and variable focus, lighting, viewpoint, resolution and depth of field; hints about shape, reflectance, and location. Instead of fixed digital snapshots and video playback, computational methods promise direct interactions to explore what we photograph.
The pre-requisites are EECS 351 (Introduction to Computer Graphics) or consent of the instructor.

EECS 395 -

Please contact the Professor teaching this course for more information

EECS 395 - Computational Auditory Scene Analysis

Computational auditory scene analysis (CASA) is the study of how a computational system can organize sound into perceptually meaningful elements. Problems in this field include source separation (splitting audio mixtures into individual sounds), source identification (labeling a source sound), and streaming (finding which sounds belong to a single explanation/event). This course is an advanced graduate course covering current research in the field.

EECS 395 - Cardiovascular Instrumentation

Please contact the Professor teaching this course for more information

EECS 395 - Measurement and Analysis of Online Social Networks

This course will cover a broad range of topics related to large-scale online social networks, including the structures of online social network graphs, dynamic evolution of these graphs, exploring how social networks could be used to improve Internet search, routing, or security.

EECS 395 - Special Topics in Machine Learning

In Winter 2010, this course covers Probabilistic Graphical Models. Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how inference and learning are performed in the models, and how the models are utilized for machine learning in practice.

EECS 395 - Computational Complexity

Please see the professor

EECS 395 - The Probabilistic Method

This course will give a basic introduction to discrete probability including random variables, expectation, variance and probabilistic inequalities. We then use these tools to show the existence of combinatorial objects with certain properties by choosing them at random and showing the property holds with positive probability, for example Ramsey graphs with no large clique or independent set.

EECS 395 - Knowledge Representation

One of the most exciting frontiers in Artificial Intelligence and Cognitive Science today is knowledge representation. Knowledge representation is at the heart of the Semantic Web, next-generation natural language understanding and dialogue systems, and virtual humans for training and entertainment. Knowledge representation plays a central role in cognitive models of semantics, reasoning, and conceptual learning. This course provides a solid understanding of the principles and practices of knowledge representation, using a combination of lecture, discussion, and hands-on exercises. We will examine basic principles of logic, focusing on tradeoffs between expressiveness and tractability, and semantic web systems. Representation methods for fundamental domains (including space, time, quantity, causality, and plans) will be discussed. Grading will be based on homework assignments related to the work done in class, plus a project.

EECS 395 - Computational Geometry

After a brief introduction to numerical computation issues, the course will continue with a sequence of canonical problem settings (e.g., Intersections; Arrangements/Duality), mostly focusing on the combinatorial aspects of the algorithms and the impact of the data structures. Each part will be casted in respective applications settings (GIS; Motion Planning; etc). The last part of the course will present several potpourri-like topics, e.g., Skeletons/Medial Axis; Davenport-Shinzel sequences.

EECS 395 - Please contact professor for course description

Please contact professor for course description

EECS 395 - Programming Languages

The Programming Languages course introduces students to tools for assessing the key aspects of programming languages: syntax, semantics, and pragmatics.

EECS 398 - Electrical Engineering Design

Design of electrical and electronic devices, circuits, and systems by the application of the engineering sciences, economics, and Institute of Electrical and Electronics Engineers or other national standards.

EECS 401 - Fundamentals of Electronic Devices

Transport phenomena in semiconductors, theory of the p-n junction, bipolar and unipolar devices, general analysis of the metal-semiconductor and MIS structures, CCD, MOSFET and bipolar transistors.

EECS 402 - Advanced Electronic Devices

Semiconductor optics, heterojunctions, quantum wells, superlattices and resonant tunneling. Field-effect and potential-effect devices. Hot-electron devices. Microwave devices.

EECS 403 - Quantum Semiconductors

Elements of wave mechanics necessary to explain band theory. Fermi-Dirac statistics, introduction to the theory of electrical conductivity in semiconductors, optical and thermal properties, diffusion of electrons, and holes in solids.

EECS 404 - Quantum Electronics

Review of quantum mechanics. Harmonic oscillator. Perturbation theory. Phonons and photons. Interaction of radiation and atomic systems. Einstein coefficients. Laser oscillation. Laser photon statistics.

EECS 405 - Advanced Photonics

Physical description of compound semiconductors; optical properties of heterostructures, quantum wells, super-lattices, quantum wires and quantum dots; physics and technology of optoelectronic devices; light emitting diodes (LEDs) and lasers.

EECS 406 - Nonlinear Optics

Nonlinear optical susceptibilities; wave propagation and coupling in nonlinear media; harmonic, sum, and difference frequency generation; parametric amplification and oscillation; phase-conjugation via four-wave mixing; self-phase modulation and solitons.

EECS 407 - Quantum Optics (same as Physics 427)

Review of quantum fields; quantization of the electro-magnetic field; photodetection theory; direct, homodyne and heterodyne detection; squeezed and photon-number state generation; application to optical communication and interferometers; introduction to quantum cryptography and quantum computation.

EECS 408 - Computational Electrodynamics

Advanced topics in the finite-difference time-domain (FDTD) method for numerical modeling of electromagnetic wave interactions with engineering structures. Reduced-numerical-dispersion algorithms employing fourth-order spatial differencing; uniaxial perfectly matched layer absorbing boundary conditions; generalized grids; incorporation of lumped-circuit elements.

EECS 409 - Semiconductor Lasers

Basic concepts of lasers; laser applications; gas and liquid lasers; solid-state lasers; semiconductor lasers; materials and devices; rate equations; laser gain and saturation; modulation and light pulse generation; advanced technology for semiconductor laser fabrications and integration; industrial and medical applications of lasers.

EECS 410 - System Theory

Unified treatment of continuous and discrete time systems from a state-variable viewpoint; emphasis on linear systems. Concept of state, writing and solving state equations, controllability and observability, transform techniques (Fourier, Laplace, Z), stability, and Lyapunov's method.

EECS 418 - Advanced Digital Signal Processing

Selected topics in digital signal processing such as digital speech processing, multidimensional digital signal processing, spectrum estimation, and error analysis.

EECS 420 - Digital Image Processing

Fundamentals of image processing. Image compression, enhancement, and restoration. Image reconstruction from projections and partial information.

EECS 421 - Multimedia Signal Processing

Fundamentals of processing multimedia signals: text, graphics, speech, audio, image, video; standards for multimedia coding, processing and compression. Related products and services will be discussed.

EECS 422 - Random Processes in Communications and Control I

Fundamentals of random variables; mean-squared estimation; limit theorems and convergence; definition of random processes; autocorrelation and stationarity; Gaussian and Poisson processes; Markov chains.

EECS 423 - Random Processes in Communications and Control II

Advanced topics in random processes: point processes, Wiener processes; Markov processes, spectral representation, series expansion of random processes, linear filtering, Wiener and Kalman filters, optimum receivers and matched filters.

EECS 424 - Noise and Fluctuation in Physical/ Engineering Systems

Fluctuation in linear and nonlinear systems via Markov random processes, Fokker-Planck-Kolmogorov equation, Langevin-Ito equation, stochastic calculus, and Feynman-Wiener integral; applications to electronic devices, lasers, communication, and control systems.

EECS 425 - Quantum Electronics II: Noise, Modulation, and Quantum Properties of Laser Emissions

Introduction to semiclassical and fully quantized theory of laser leading to noise, coherent, and modulation properties of laser light, with emphasis on semiconductor laser. The quantum concept of photon and photodetection will be formulated. The theory will be applied to describing noise in optical amplifier and photodetection. The effect of optical feedback on laser will be covered. The inhibition and enhancement of spontaneous emission in microcavity and nanophotonic device structures will be discussed.

EECS 426 - Signal Detection and Estimation

Simple-hypothesis detection problems, detection of signals with unknown parameters, Bayes' maximum likelihood estimation, estimation of signal parameters, detection of stochastic signals, nonparametric detection and estimation.

EECS 427 - Optical Communications

Optical communication systems, optical wave propagation, photodetection statistics, heterodyne receiver, and noise sources. Evaluation of communication performance for the free-space channel. Introduction to fiber optic communication and fiber optic networks.

EECS 428 - Information Theory

Information measures and their properties: entropy, divergence, mutual information, channel capacity. Shannon's fundamental theorems for data compression and coding for noisy channels. Applications in communications, statistical inference, algorithmic complexity, probability, and finance.

EECS 429 - Selected Topics in Quantum Information Science and Technology

Basic general principles of quantum mechanics for applications to quantum information science and technology. The fundamentals will be covered together with topics of current interest among the areas of quantum teleportation, quantum computation, and quantum cryptography.

EECS 432 - Advanced Computer Vision

Advanced topics in computer vision including low-level vision, geometrical and 3D vision, stereo, 3D scene reconstruction, motion analysis, visual tracking, object recognition and human motion analysis, capturing and recognition, with the applications to video processing and vision-based modeling and interaction.

EECS 435 - Neural Networks

Learning in one-layer and multi-layer feed-forward networks, recurrent networks and dynamical systems. Perceptrons, Hebbian learning, associative memories, Widrow-Hoff learning, backpropagation networks, radial basis function networks, competitive networks, counterprapagation networks, Grossberg network, Adaptive resonance theory, Hopfield networks, simulated annealing, Boltzmann machine.

EECS 438 - Interdisciplinary Nonlinear Dynamics

438-1: Example-oriented survey of nonlinear dynamical systems, including chaos, combining numerical, analytical and geometrical approaches to differential equations. 438-2,3: Interdisciplinary theoretical, computational and experimental projects involving complex systems in science and engineering, directed by a cross-disciplinary faculty team.

EECS 440 - Advanced Networking

This course will cover a broad range of topics including Internet evolution and architectures; analysis and design of network protocols (both wired and wireless); networking issues for Web and gaming applications; analysis and performance of content distribution networks; network security, vulnerability, and defenses.

EECS 441 - Resource Virtualization

Virtual machines of all kinds, virtual networking, virtual services, virtual storage, emulation, distributed computing using virtualized resources.

EECS 442 - Dynamic Behavior of Applications, Hosts, and Networks

Students will learn the current state of theory and practice in workload characterization, and use what they learn to measure, analyze, model, and predict the dynamic behavior of distributed computing environments and their applications.

EECS 443 - Advanced Operating Systems

Advanced Operating Systems is a graduate level course on advanced concepts on operating systems and distributed computing. The course covers a wide array of research topics in systems, from historical perspectives to current topics such as peer-to-peer computing and mobile systems. The class consists of two major thrusts: reading and reviewing papers and doing a research project on your own.

EECS 450 - Internet Security

The evolution of Internet has spawned rich complexity and vulnerability in its infrastructure. In this course, we will take a measurement-based approach to understand the complexity of the Internet, i.e., characterize, understand, and model the enormous volume and great variety of Internet traffic in terms of large-scale behaviors. Based on that, we will investigate the vulnerability of the Internet when different services have evolved and innovated in different and competing ways, with increasingly less global consensus. We will start with the basic concepts of Internet architecture, its design principles and evolution, and then focus on security challenges of network and distributed systems as well as the counter-attack approaches.

During the course, we will read and discuss research papers, and identify a list of open research problems, from which the students can choose their class projects. In addition to deploying end-to-end measurement on global network testbed, PlanetLab (http://www.planet-lab.org), massive real-world anonymized router/gateway traffic data will be obtained to analyze the reliability/vulnerability of the Internet and to detect both well-known and unknown virus/worm/attacks. We will further characterize and diagnose the unknown anomalies and network failures.

EECS 452 - Advanced Computer Architecture I

Design and evaluation of modern uniprocessor computing systems. Evaluation methodology/metrics and caveats, instruction set design, advanced pipelining, instruction level parallelism, prediction-based techniques, alternative architectures (VLIW, Vector and SIMD), memory hierarchy design, I/O, and recent trends in architecture (e.g., low-power architectures, application-specific processors). Case studies.

EECS 453 - Advanced Computer Architecture II

Parallel computer architecture and programming models. Message passing and shared memory multiprocessors. Scalability, synchronization, memory consistency, cache coherence. Memory hierarchy design. Network design.

EECS 454 - Modeling and Analysis of Communication Networks

Basic techniques for modeling and analyzing data communication networks. Protocol specification and correctness, queuing models, loss networks, multi-class queues and scheduling, graph-based and flow-based routing, congestion control and pricing.

EECS 455 - Distributed Computing Systems

Fundamentals and systems design aspects of distributed systems, paradigms for distributed computing, client-server computing, concurrency control, distributed file systems, resource management, high-performance computing aspects.

EECS 457 - Advanced Algorithms

Design and analysis of advanced algorithms: graph algorithms; maximal network flows; min-cost flow algorithms; convex cost flows.

EECS 459 - VLSI Algorithmics

Design and analysis of algorithms for VLSI synthesis problems. Study both theoretical and practical aspects of CAD-tool development in VLSI environments.

EECS 463 - Adaptive Filters

Applications of adaptive filtering, autoregressive and moving average processes, linear prediction, lattice filters, Least Mean Square (LMS) algorithm, least squares filtering, convergence analysis.

EECS 467 - Parallel and Distributed Database Systems

File allocation and load balancing in parallel I/O systems. Distributed, scalable file systems. Declustering and range partitioning. Parallel processing of relational queries: sort, clustering and join algorithms. Distributed database systems architectures. Query processing in distributed database systems: Processing simple queries; using semi-joins and joins for general queries.

EECS 472 - Designing and Constructing Models with Multi-Agent Languages

This course focuses on the exploration, construction and analysis of multi-agent models. Sample models from a variety of content domains are explored and analyzed. Spatial and network topologies are introduced. The prominent agent-based frameworks are covered as well as methodology for replicating, verifying and validating agent-based models. We use state of the art ABM and complexity science tools. This course can help satisfy the project course and artificial intelligence area course requirement for CS and CIS majors, and satisfy the breadth requirement in artificial intelligence for Ph.D. students in CS. It also satisfies a design course requirement for Learning Sciences graduate students, counts towards the Cognitive Science specialization and as an advanced elective for the Cognitive Science major.

EECS 478 - Advanced Digital Communications

Digital modulation, complex base band signaling, sequence estimation, the Viterbi algorithm, probability of error analysis, equalization, and code-division multiple access.

EECS 479 - Nonlinear Optimization

Numerical solution of unconstrained optimization problems, nonlinear least squares and nonlinear systems of algebraic equations, large-scale nonlinear optimization, quadratic programming, and constrained optimization.

EECS 493 - Design and Analysis of High-Speed Integrated Circuits

Issues that arise in the design and analysis of VLSI circuits at high speeds such as buffer sizing, repeater insertion, noise, electromigration, Elmore delay, scaling trends, and power consumption. Cross-listed as EECS 393.

EECS 495 - The Probabilistic Method

This course will give a basic introduction to discrete probability including random variables, expectation, variance and probabilistic inequalities. We then use these tools to show the existence of combinatorial objects with certain properties by choosing them at random and showing the property holds with positive probability, for example Ramsey graphs with no large clique or independent set.

EECS 495 - Please contact professor for course description

Please contact professor for course description

EECS 495 - Advanced Algorithms

Please see instructor for the course description.

EECS 495 - Programming Languages Seminar

This is a course where students learn to read research papers in programming languages. The particular topic of papers changes from offering to offering. Contact Prof.Findler for details on a specific offering.

EECS 495 - Knowledge Representation

One of the most exciting frontiers in Artificial Intelligence and Cognitive Science today is knowledge representation. Knowledge representation is at the heart of the Semantic Web, next-generation natural language understanding and dialogue systems, and virtual humans for training and entertainment. Knowledge representation plays a central role in cognitive models of semantics, reasoning, and conceptual learning. This course provides a solid understanding of the principles and practices of knowledge representation, using a combination of lecture, discussion, and hands-on exercises. We will examine basic principles of logic, focusing on tradeoffs between expressiveness and tractability, and semantic web systems. Representation methods for fundamental domains (including space, time, quantity, causality, and plans) will be discussed. Grading will be based on homework assignments related to the work done in class, plus a project.

EECS 495 - Computational Auditory Scene Analysis

Computational auditory scene analysis (CASA) is the study of how a computational system can organize sound into perceptually meaningful elements. Problems in this field include source separation (splitting audio mixtures into individual sounds), source identification (labeling a source sound), and streaming (finding which sounds belong to a single explanation/event). This course is an advanced graduate course covering current research in the field.

EECS 495 - Cardiovascular Instrumentation

Please contact the Professor teaching this course for more information

EECS 495 - Measurement and Analysis of Online Social Networks

This course will cover a broad range of topics related to large-scale online social networks, including the structures of online social network graphs, dynamic evolution of these graphs, exploring how social networks could be used to improve Internet search, routing, or security.

EECS 495 - Computational Photography

Computational photography combines plentiful low-cost computing, digital sensors, actuators, and lights to escape the limitations of traditional film-like methods. New methods offer unbounded dynamic range and variable focus, lighting, viewpoint, resolution and depth of field; hints about shape, reflectance, and location. Instead of fixed digital snapshots and video playback, computational methods promise direct interactions to explore what we photograph.
The pre-requisites are EECS 351 (Introduction to Computer Graphics) or consent of the instructor. The pre-requisites are EECS 351 (Introduction to Computer Graphics) or consent of the instructor.

EECS 495 - Special Topics in Machine Learning

In Winter 2010, this course covers Probabilistic Graphical Models. Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how inference and learning are performed in the models, and how the models are utilized for machine learning in practice.

EECS 495 - The Probabilistic Method

This course will give a basic introduction to discrete probability including random variables, expectation, variance and probabilistic inequalities. We then use these tools to show the existence of combinatorial objects with certain properties by choosing them at random and showing the property holds with positive probability, for example Ramsey graphs with no large clique or independent set.

EECS 495 - Please contact professor for course description

Please contact professor for course description

EECS 495 - Please contact professor for course description

Please contact professor for course description

EECS 495 - Please contact professor for course description

Please contact professor for course description

EECS 495 - Please contact professor for course description

Please contact professor for course description

EECS 495 - Programming Languages

The Programming Languages course introduces students to tools for assessing the key aspects of programming languages: syntax, semantics, and pragmatics.

EECS 510 - Please contact professor for course description

Please contact professor for course description

EECS 510 - Human Perception and Electronic Media

Fundamentals of visual, acoustic, and tactile perception; display devices; perceptual models for image, video, acoustic, and tactile signal analysis, compression, quality evaluation, and understanding; multimodal signal processing and perception; content-based retrieval; sense substitution.

EECS 510 - Please contact professor for course description

Please contact professor for course description