Binary options streaming signals catalog four-letter course codes-undergraduate academic catalogs
Optical sensors including single element and area arrays CCDs ; optical systems including imagers, spectrometers, interferometers, and lidar; optical principles and light gathering power; electromagnetics of atomic and molecular emission and scattering with applications to the atmosphere the prime example; applications to ground and spacecraft platforms.
Four laboratory sessions 4. Circuits and devices used for switching power converters, solid-state motor drives, and power controllers; dc-dc, ac-dc, and dc-ac converters and applications; high-power transistors and magnetic components; design considerations including heat transfer.
Fundamentals of robotics including rigid motions; homogeneous transformations; forward and inverse kinematics; velocity kinematics; motion planning; trajectory generation; sensing, vision; control. Theoretical and engineering foundations of ultrasonic imaging for medical diagnostics. Conventional, Doppler, and advanced ultrasonic imaging techniques; medical applications of different ultrasonic imaging techniques; engineering problems related to characterization of ultrasonic sources and arrays, image production, image quality, the role of contrast agents in ultrasonic imaging, and system design.
Development of the basic theoretical concepts of acoustical systems; mechanical vibration, plane and spherical wave phenomena in fluid media, lumped and distributed resonant systems, and absorption phenomena and hearing.
Development of power system equivalents by phase network analysis, load flow, symmetrical components, sequence networks, fault analysis, and digital simulation. Fundamental physical, mathematical, and computational principles governing the data acquisition and image reconstruction of magnetic resonance imaging.
Fundamental physical properties of nanoscale systems. Nanofabrication techniques, semiconductor nanotechnology, molecular and biomolecular nanotechnology, carbon nanotechnology nanotubes and graphene , nanowires, and nanoscale architectures and systems.
Basic linear integrated circuit design techniques using bi-polar, JFET, and MOS technologies; operational amplifiers; wide-band feedback amplifiers; sinusoidal and relaxation oscillators; electric circuit noise; application of linear integrated circuits.
Principles of advanced methods of pattern delineation, pattern transfer, and modern material growth; how these are applied to produce novel and high performance devices and circuits in various electronic materials with special emphasis on semiconductors. Computer simulation of processes and the manufacturing of devices and circuits.
Introduction to principles, fabrication techniques, and applications of microelectromechanical systems MEMS. In-depth analysis of sensors, actuator principles, and integrated microfabrication techniques for MEMS. Comprehensive investigation of state-of-the-art MEMS devices and systems. Analysis and design of control systems with emphasis on modeling, state variable representation, computer solutions, modern design principles, and laboratory techniques.
Application of quantum mechanical concepts to electronics problems; detailed analysis of a calculable two-state laser system; incidental quantum ideas bearing on electronics. Advanced semiconductor materials and devices; elementary band theory; heterostructures; transport issues; three-terminal devices; two-terminal devices; including lasers and light modulators. Basic theory and methods for the solution of optimization problems; iterative techniques for unconstrained minimization; linear and nonlinear programming with engineering applications.
Active photonic devices and lightwave technology. Hands-on experience with several classes of lasers HeNe laser, semiconductor edge emitting lasers, vertical cavity surface emitting lasers , photodetectors, and photonic systems. Familiarization with experimental optical characterization techniques and equipment.
Individual research project under the guidance of a faculty member: Preparation of a written research proposal, including preliminary results.
Preparation and oral presentation of a written thesis that reports the results of the project. Algorithm techniques for enhancing the scalability of parallel software: Comprehensive foundation in the broad field of micro and nanolithography; the science of optical imaging, photochemistry, and materials issues; technological developments including state-of-the-art commercial lithography systems.
Applications of micro and nanolithography to diverse fields including: Advanced concepts in computer architecture: Emphasis on hardware alternatives in detail and their relation to system performance and cost. Design of high performance computer systems; instruction level concurrency; memory system implementation; pipelining, superscalar, and vector processing; compiler back-end code optimization; profile assisted code transformations; code generation and machine dependent code optimization; cache memory design for multiprocessors; synchronization implementation in multiprocessors; compatibility issues; technology factors; state-of-the-art commercial systems.
Mathematical tools in a vector space framework, including: Feedback control systems emphasizing state space techniques. Basic principles, modeling, analysis, stability, structural properties, optimization, and design to meet specifications.
Design of nonlinear control systems based on stability considerations; Lyapunov and hyperstability approaches to analysis and design of model reference adaptive systems; identifiers, observers, and controllers for unknown plants.
Semiconductor nanotechnology from the formation and characterization of low-dimensional structures to device applications. Compound semiconductors, epitaxial growth, quantum dots, nanowires, membranes, strain effect, quantum confinement, surface states, 3D transistors, nanolasers, multijunction tandem solar cells, and nanowire thermoelectrics.
Handouts are supplemented with papers from the research literature. Critical literature review assignments, research proposals in National Science Foundation format, and oral presentations are required. This course teaches algorithms for verification that are applied to very large scale hardware in the chip design industry.
The course teaches symbolic model checking, Binary decision diagrams BDDs , satisfiability SAT based algorithms, symbolic simulation, coverage metrics for simulation, automatic assertion generation, analog circuit verification and post Silicon validation algorithms.
The course teaches scalable search algorithms that can be applied to discrete and continuous space models. Fundamental electromagnetic theory with applications to plane waves, waveguides, cavities, antennas, and scattering; electromagnetic principles and theorems; and solution of electromagnetic boundary-value problems. Basic concepts and techniques, both theoretical and experimental, applicable to gaseous electronics, gas and solid plasmas, controlled fusion, aeronomy, gas lasers, and magnetohydrodynamics.
Theoretical aspects of distributed algorithms, with an emphasis on formal proofs of correctness and theoretical performance analysis. Algorithms for consensus, clock synchronization, mutual exclusion, debugging of parallel programs, peer-to-peer networks, and distributed function computation; fault-tolerant distributed algorithms; distributed algorithms for wireless networks.
Class projects focusing on current SOC design and research. Nonlinear dynamics, vector fields and flows, Lyapunov stability theory, regular and singular perturbations, averaging, integral manifolds, input-output and input-to-state stability, and various design applications in control systems and robotics. Fundamental techniques for the analysis of large-scale electrical systems, including methods for nonlinear and switched systems.
Emphasis on the importance of the structural characteristics of such systems. Key aspects of static and dynamic analysis methods. Propagation of electromagnetic waves in general cylindrical waveguides; stationary principles; non-uniform inhomogeneously filled waveguides; mode and power orthogonality; losses in waveguides; analytical and numerical techniques; microwave integrated circuits waveguides; optical waveguides.
Compound semiconductor materials and their optical properties. Diode lasers including quantum well heterostructure lasers, strained layer lasers, and quantum wire and quantum dot lasers. Current topics in diode laser development. Basic concepts of random processes; linear systems with random inputs; Markov processes; spectral analysis; Wiener and Kalman filtering; applications to systems engineering.
Introductory quantum mechanics of semiconductors; energy bands; dynamics of Block electrons in static and high-frequency electric and magnetic fields; equilibrium statistics; transport theory, diffusion, drift, and thermoelectric effects; characteristics of p-n junctions, heterojunctions, and transistor devices. Senior-level course in quantum mechanics or atomic physics. Integrated optical and optoelectronic devices; theory of optical devices including laser sources, waveguides, photodetectors, and modulations of these devices.
Development of an intuitive understanding of speech processing by the auditory system, in three parts. The theory of acoustics of speech production, introductory acoustic phonetics, inhomogeneous transmission line theory and reflectance , room acoustics, the short-time Fourier Transform and its inverse , and signal processing of speech LPC, CELP, VQ. Psychoacoustics of speech perception, critical bands, masking JNDs , and the physiology of the auditory pathway cochlear modeling.
Information theory entropy, channel capacity, the confusion matrix, state models, EM algorithms, and Bayesian networks. Presentation of classic papers on speech processing and speech perception by student groups. Advanced topics of current interest in the physics of semiconductors and solid-state devices.
Basic computational techniques for numerical analysis of electromagnetics problems, including the finite difference, finite element, and moment methods.
Emphasis on the formulation of physical problems into mathematical boundary-value problems, numerical discretization of continuous problems into discrete problems, and development of rudimentary computer codes for simulation of electromagnetic fields in engineering problems using each of these techniques. Development of analytical models of computer systems and application of such models to performance evaluation: Advanced concepts in hardware and software fault tolerance: Advanced graduate course on modern probabilistic theory of adaptive and learning systems.
The following topics will be covered; basics of statistical decision theory; concentration inequalities; supervised and unsupervised learning; empirical risk minimization; complexity-regularized estimation; generalization bounds for learning algorithms; VC dimension and Rademacher complexities; minimax lower bounds; online learning and optimization.
Along with the general theory, the course will discuss applications of statistical learning theory to signal processing, information theory, and adaptive control.
Basic prerequisites include probability and random processes, calculus, and linear algebra. Other necessary material and background will be introduced as needed.
Lectures and discussions related to advanced topics and new areas of interest in signal processing: May be repeated 8 hours in a term to a total of 20 hours.
Credit towards a degree from multiple offerings of this course is not given if those offerings have significant overlap, as determined by the ECE department. As specified each term.
It is expected that each offering will have a level course as prerequisite or co-requisite. Advanced topics in acoustics including physical properties of a fluid; linear propagation phenomena; nonlinear phenomena such as radiation force, streaming, and harmonic generation; cavitation; absorption and dispersion.
Signal integrity aspects involved in the design of high-speed computers and high-frequency circuits; addressing the functions of limitations of interconnects for system-level integration.
Topics explored include packaging structures, power and signal distribution, power level fluctuations, skin effect, parasitics, noise, packaging hierarch, multilayer wiring structures as well as the modeling and simulation of interconnects through the use of computer-aided design CAD and computational electromagnetics. Fundamental concepts, techniques, and directions of research in image processing: Information processing approaches to computer vision, algorithms, and architectures for artificial intelligence and robotics systems capable of vision: Computational approaches to robot motion planning, configuration space, algebraic decompositions, artificial potential fields, retraction, approximate decompositions, planning under uncertainty, grasp planning, and task-level planning.
Basic concept review of digital signals and systems; computer-aided digital filter design, quantization effects, decimation and interpolation, and fast algorithms for convolution and the DFT; introduction to adaptive signal processing. Formulation of circuit equations; sparse matrix algorithms for the solution of large systems, AC, DC, and transient analysis of electrical circuits; sensitivity analysis; decomposition methods.
Theoretical and algorithmic foundations of deterministic optimal control theory, including calculus of variations, maximum principle, and principle of optimality; the Linear-Quadratic-Gaussian design; differential games and H-infinity optimal control design.
Reliability and dynamic performance evaluation for large-scale and complex systems; building on system-theoretic modeling, analysis, and design techniques. Design methods for reliability including architecture design and filter-based fault detection and isolation.
Analytical methods for optimal redundancy allocation, sensitivity analysis methods for iterative system design, and other techniques for design optimization. Mechatronic systems used in aircraft and automotive, power electronic systems, and electrical power systems are examples of applications discussed.
Stochastic control models; development of control laws by dynamic programming; separation of estimation and control; Kalman filtering; self-tuning regulators; dual controllers; decentralized control. Coding theory with emphasis on the algebraic theory of cyclic codes using finite field arithmetic, decoding of BCH and RS codes, finite field Fourier transform and algebraic geometry codes, convolutional codes, and trellis decoding algorithms.
Multidimensional signals, convolution, transforms, sampling, and interpolation; design of two-dimensional digital filters; sensor array processing and range-doppler imaging; applications to synthetic aperture radar, optics, tomography, radio astronomy, and beam-forming sonar; image estimation from partial data.
Lectures and discussion related to advanced topics and new areas of interest in the theory of communication systems: May be repeated in the same term, if topics vary, to a maximum of 12 graduate hours; may be repeated in separate terms, if topics vary, to a maximum of 16 graduate hours. Credit toward a degree from multiple offerings of this course is not given if those offerings have significant overlap, as determined by the ECE department.
It is expected that each offering will have a level course as a prerequisite or co-requisite. Homework and a term project apply these concepts in the design of VLSI architectures for digital signal processing and communication systems. Detection and estimation theory, with applications to communication, control, and radar systems; decision-theory concepts and optimum-receiver principles; detection of random signals in noise, coherent and noncoherent detection; parameter estimation, linear and nonlinear estimation, and filtering.
Digital communication systems modulation, demodulation, signal space methods, channel models, bit error rate, spectral occupancy, synchronization, equalization, trellis-coded modulation, wireless channels, multiantenna systems, spread spectrum, and orthogonal frequency modulation. Mathematical models for channels and sources; entropy, information, data compression, channel capacity, Shannon's theorems, and rate-distortion theory.
Current research topics in modern light microscopy: Power dissipation in modern electronics, from fundamentals to system-level issues. Energy transfer through electrons and phonons, mobility and thermal conductivity, power dissipation in modern devices CMOS, memory, nanowires, nanotubes , circuit leakage, thermal breakdown, interconnects, thermometry, heat sinks. Handouts are supplemented with papers from the research literature, Wikipedia assignments, a final conference-type group paper, and oral presentations required.
Computational inference and machine learning have seen a surge of interest in the last 15 years, motivated by applications as diverse as computer vision, speech recognition, analysis of networks and distributed systems, big-data analytics, large-scale computer simulations, and indexing and searching of very large databases.
This course introduces the mathematical and computational methods that enable such applications. Topics include computational methods for statistical inference, sparsity analysis, approximate inference and search, and fast optimization.
Performance analysis and design of multiple-user communication systems; emphasis on rigorous formulation and analytical and computational methods; includes queuing networks, decentralized minimum delay routing, and dynamic network flow control. Fundamental electrical and mechanical laws for derivation of machine models; simplifying transformations of variables in electrical machines; power electronics for motor control; time-scale separation; feedback linearization and nonlinear control as applied to electrical machines.
Typical electromechanical applications in actuators, robotics, and variable speed drives. Physical optics, solution of linear inverse problems, and computed imaging. Forward problems in diffraction, asymptotics, ray propagation, x-ray projections, scattering, sources, optical coherence tomography, and near-field optics. Solution of associated inverse problems including back-propagation, back-projection, Radon transforms x-ray CT , inverse scattering, source localization, interferometric synthetic aperture microscopy, and near-field tomography.
Special topics as time permits. Light propagation in anisotropic crystals; second- and third-order nonlinear susceptibility and electro-optic effect; discussion of the relationship of these effects along with such applications as light modulation, harmonic generation, and optical parametric amplification and oscillation.
Electromagnetic waves in layered media; plane wave expansion of electromagnetic point source field; Sommerfeld integrals; transient response; WKB method with asymptotic matching; scattering by junction discontinuity; surface integral equation; volume integral equation; inverse problems.
Theoretical approach to quantum mechanics and atomic physics, with many applications in spin resonance and modern maser theory. Energy control center functions, state estimation and steady state security assessment techniques, economic dispatch, optimal power flow, automatic generation control, and dynamic equivalents.
Nanoscale interaction between light and semiconductors, metals, or composites; plasmonics, cavity electrodynamics, polarition cavity condensation, sub-wavelength structures, metamaterials, and applications. Detailed modeling of the synchronous machine and its controls, such as excitation system and turbine-governor dynamics; time-scales and reduced order models; non-linear and linear multi-machine models; stability analysis using energy functions; power system stabilizers.
Selected topics from recent engineering literature on antennas supplemented by advanced topics in electromagnetic theory needed for comprehension; current techniques for analysis of wire, slot, horn, frequency independent, quasi-optical, and array antennas.
Normed, Banach, and Hilbert spaces; applications of the projection theorem and the Hahn-Banach Theorem to problems of minimum norm, least squares estimation, mathematical programming, and optimal control; the Kuhn-Tucker Theorem and Pontryagin's maximum principle; iterative methods. Advanced topics in modern analog IC design. Emphasis on CMOS building blocks and circuit techniques as a result of fabrication technology advancement.
Noise in linear analog circuits; linear feedback theory and stability; harmonic distortion in weakly nonlinear circuits; switched-capacitor circuit technique and realization; Nyquist-rate and oversampled data converters.
Extensive computer simulations required in both homework and final project. Basic physical design requirements for VLSI; performance-oriented formulation and optimization of chip partitioning, module placement and interconnection; optimized design and layout of on-chip modules; circuit extraction; high-speed VLSI circuits; yield and reliability analysis; advanced VLSI packaging and parametric testing.
Examines formal analysis an synthesis approaches for discrete, continuous, and hybrid models of computing systems and their physical environment. Use of context, structure, and prediction to improve compression. Fourier and discrete cosine transforms, wavelet transforms, quantization. Fidelity and distortion metrics, rate-distortion analysis. Image, video, and audio compression standards.
Neurobotics The field of Neurobotics lies at the intersection of robotics and medicine. It aims to build a robot-human closed loop system to alter the neural control of movement as a way to rehabilitate, assist, and enhance human motor control and learning capabilities. Typically, the primary target population is individuals with strokes, spinal cord injuries, traumatic brain injuries, and other injuries that inhibit daily activities. However, it could also target sports medicine, military, and entertainment applications.
This course is an introductory design course in Neurobotics focusing on learning about human neural control of movement, using physiological signals as inputs, and controlling a mechanical device.
Students will learn simple control laws, hands on experience and programming in controlling robots, and applying knowledge of human movements to move the robot. There is a design project competition at the end of quarter. Computer Telephony Computer Telephony. Educational Software Capstone Software to support learning comprises a wide variety of styles and scales of programming. From games for children to large online learning systems, educational software not only touches on many areas of computer science but depends also on psychology, communication, design, and the fields of study the software is to support.
Developing software for learning can be both highly exciting and very challenging. This capstone course will blend a study of key aspects of educational software with group projects that allow students to explore the issues in a hands-on manner. Intellectual Property Law for Engineers This course will provide a survey of intellectual property law for a technical non-legal audience, with a primary focus on patent law.
The purpose of the course is to assist engineers and scientists in navigating and utilizing various intellectual property regimes effectively in the business context. In the patent realm, topics will include patent preparation and prosecution, patent claim interpretation, and assessing patent validity and infringement. Other intellectual property areas that may be covered, time permitting, include copyright, trademark, and trade secret law.
Where possible, the course will also endeavor to balance the discussion of practical legal considerations with broader policy questions e. Data Science and Society Seminar Current topics related to the societal implications of data science.
Topic selection will vary from quarter to quarter and may include data privacy and security, data anonymization, hypothesis-testing on a shared database, impact of data science-based decisions on society. Includes both guest speakers and case-study or article-based discussions. Undergraduate Research Seminar Students prepare and give a public talk on their faculty-sponsored research projects. Senior Project A report and perhaps demonstration describing a development, survey, or small research project in computer science or an application to another field.
Work normally extends over more than one quarter, for a maximum of 6 credits for ; 9 credits are required for H. Reading And Research Available in special situations for advanced computer science majors to do reading and research in field, subject to approval of undergraduate adviser and CSE faculty member.
Free elective, but does not replace core course or computer science elective. Compiler Construction Principles and practice of building efficient implementations of modern programming languages. Lexical, syntactic, and semantic analysis of programs. Intra- and interprocedural analysis and optimization. Related programming environment facilities such as source-level debuggers and profilers.
Principles Of Software Engineering Study of major developments in software engineering over the past three decades. Advanced Topics In Software Systems Topics include software architecture, software tools, programming language analysis, type systems, formal reasoning, and other pertinent topics in software engineering and programming languages research.
Programming Languages A study of non-imperative programming paradigms such as functional, object-oriented, logic, and constraint programming. Programming language semantics and type theory. Advanced Topics in Programming Languages May include functional, object-oriented, parallel, and logic programming languages; semantics for languages of these kinds; type declaration, inference, and checking including polymorphic types ; implementation issues, such as compilation, lazy evaluation, combinators, parallelism, various optimization techniques.
Human Computer Interaction Topics in human-computer interaction, including tools and skills for user interface design, user interface software architecture, rapid prototyping and iterative design, safety and critical systems, evaluation techniques, and computer supported cooperative work. Natural Language Processing Overview of modern approaches for natural language processing. Topics include language models, text classification, tagging, parsing, machine translation, semantics, and discourse analysis.
Applied Algorithms Principles of design of efficient algorithms with emphasis on algorithms with real world applications. Examples drawn from computational geometry, biology, scientific computation, image processing, combinatorial optimization, cryptography, and operations research.
Parallel Computation Survey of parallel computing including the processing modes of pipelining, data parallelism, thread parallelism, and task parallelism; algorithmic implications of memory models; shared memory and message passing; hardware implementations; bandwidth and latency; synchronization, consistency, interprocessor communication; programming issues including implicit and explicit parallelism, locality, portability. Computational Biology Introduction to the use of computational methods for understanding biological systems at the molecular level.
Problem areas such as mapping and sequencing, sequence analysis, structure prediction, phylogenic inference, motif discovery, expression analysis, and regulatory analysis. Techniques such as dynamic programming, Markov models, MCMC, expectation-maximization, and local search. Computability And Complexity Theory Survey of the theory of computation including Turing Machines, Churche' s Thesis, computability, incompleteness, undecidability, complexity classes, problem reductions, Cook' s theorem, NP-completeness, randomized computation, cryptography, parallel computation, and space complexity.
Some emphasis will be placed on historical and philosophical aspects of the theory of computation. Database Management Systems Introduction to the principles of database management systems.
Topics include database system architecture, data models, theory of database design, query optimization, concurrency control, crash recovery, and storage strategies. Transaction Processing Technology supporting reliable large-scale distributed computing, including transaction programming models, TP monitors, transactional communications, persistent queuing, software fault tolerance, concurrency control and recovery algorithms, distributed transactions, two-phase commit, data replication.
Data Mining Methods for identifying valid, novel, useful, and understandable patterns in data. Computer Architecture Architecture of the single-chip microprocessor: Computer Operating Systems A study of developments in operating systems from the ' s to the present. Distributed Systems Principles, techniques, and examples related to the design, implementation, and analysis of distributed computer systems. Current Trends In Computer Graphics Introduction to computer image synthesis, modeling, and animation emphasizing the state-of-the-art algorithm applications.
Topics may include visual perception, image processing, geometric transformations, hierarchical modeling, hidden-surface elimination, shading, ray-tracing, anti-aliasing, texture mapping, curves, surfaces, particle systems, dynamics, realistic character animation, and traditional animation principles. Network Systems Current choices and challenges in network systems. Design And Implementation Of Digital Systems Overview of current implementation technologies for digital systems including custom integrated circuits, field-programmable logic, and embedded processors.
Systems components such as buses and communications structures, interfaces, memory architectures, embedded systems, and application-specific devices. Focus on the design of large systems using modern CAD tools.
Applications Of Artificial Intelligence Introduction to the use of Artificial Intelligence tools and techniques in industrial and company settings. Topics include foundations search, knowledge representation and tools such as expert systems, natural language interfaces and machine learning techniques. Computer Vision Provides an overview of computer vision, emphasizing the middle ground between image processing and artificial intelligence. Image formation, pre-attentive image processing, boundary and region representations, and case studies of vision architectures.
Design and Implementation of Digital Systems Overview of current implementation technologies for digital systems including custom integrated circuits, field-programmable logic, and embedded processors.
Topics vary by quarter. Software Entrepreneurship Provides an overview of the major elements of entrepreneurial activity in software, including market identification and analysis, evaluation and planning of the business, financing, typical operating and administrative problems, and alternatives for growth or sale. Performance Analysis This course is intended to provide a broad introduction to computer system performance evaluation techniques and their application.
Applications of the techniques are studied using case study papers. Database Group Meeting Database group meeting. Primarily, it consists of reading and discussing papers in this field and related fields. It is open to all students interested in education. Robotics Lab Group Meeting We discuss recent developments in robotics, focusing on probabilistic techniques and multi-robot collaboration.
Quantum Computing Journal Club Our group studies many aspects of quantum computing, from ideas about how to build a quantum computer, to the quantum algorithms that will run on these future devices, as well as the greater implications that this intersection of physics and computer science has for both subjects.
Programming Language Analysis And Implementation Design and implementation of compilers and run-time systems for imperative, object-oriented, and functional languages. Intra- and interprocedural analyses and optimizations. Software Engineering Specification, implementation, and testing of large, multiperson, software systems. Topics include abstraction, information hiding, software development environments, and formal specifications.
Advanced Topics In Software Engineering Topics vary but may include software design and evolution, formal methods, requirements specifications, software and system safety, reverse engineering, real-time software, metrics and measurement, programming environments, and verification and validation. CSE major or permission of instructor. Principles Of Programming Languages Design and formal semantics of modern programming languages, includes functional and object-oriented languages.
Advanced Topics In Programming Languages May include functional, object-oriented, parallel, and logic programming languages; semantics for languages of these kinds; type declaration, inference, and checking including polymorphic types ; implementation issues, such as compilation, lazy evaluation, combinators, parallelism, various optimization techniques.
Computer-Aided Reasoning for Software Covers theory, implementation, and applications of automated reasoning techniques, such as satisfiability solving, theorem proving, model checking, and abstract interpretation. Topics include concepts from mathematical logic and applications of automated reasoning to the design, construction, and analysis of softwar.
Advanced Topics In Human-computer Interaction Content varies, including interface issues for networks, embedded systems, education applications, safety and critical systems, graphics and virtual reality, databases, and computer-supported cooperative work. Data Visualization Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology and cognitive science. Lectures, reading and project. CSE or or equivalent.
Statistical Methods In Computer Science Introduction to the probabilistic and statistical techniques used in modern computer systems. Graphical models, probabilistic inference, statistical learning, sequential models, decision theory. Current Research In Computer Science Weekly presentations on current research activities by members of the department.
Only Computer Science graduate students may register, although others are encouraged to attend. Computer Science Colloquium Weekly public presentations on topics of current interest by visiting computer scientists. Correctness and analysis of algorithms. Content varies and may include such topics as algebraic algorithms, combinational algorithms, techniques for proving lower bounds on complexity, and algorithms for special computing devices such as networks or formulas.
Computational Geometry Algorithms for discrete computational geometry. Geometric computation, range searching, convex hulls, proximity, Vornoi diagrams, intersection.
Application areas include VLSI design and computer graphics. CSE or equivalent. Parallel Algorithms Design and analysis of parallel algorithms: Emphasis on general techniques and approaches used for developing fast and efficient parallel algorithms and on limitations to their efficacy.
Computational Biology Introduces computational methods for understanding biological systems at the molecular level. Problem areas such as network reconstruction and analysis, sequence analysis, regulatory analysis and genetic analysis. Techniques such as Bayesian networks, Gaussian graphical models, structure learning, expectation-maximization.
Computational Neuroscience Introduction to computational methods for understanding nervous systems and the principles governing their operation.
Topics include representation of information by spiking neurons, information processing in neural circuits, and algorithms for adaptation and learning. Neural Control Of Movement: A Computational Perspe Systematic overview of sensorimotor function on multiple levels of analysis, with emphasis on the phenomenology amenable to computational modeling.
Topics include musculoskeletal mechanics, neural networks, optimal control and Bayesian inference, learning and adaptation, internal models, and neural coding and decoding. Computational Complexity I Deterministic and nondeterministic time and space complexity, complexity classes, and complete problems.
Time and space hierarchies. Alternation and the polynomial-time hierarchy. Exponential complexity lower bounds. Computational Complexity Ii Advanced computational complexity including several of the following: CSE majors only; Recommended: Advanced Topics In Complexity Theory An in-depth study of advanced topics in computational complexity. Theory Of Distributed Computing Formal approaches to distributed computing problems. Topics vary, but typically include models of distributed computing, agreement problems, impossibility results, mutual exclusion protocols, concurrent reading while writing protocols, knowledge analysis of protocols, and distributed algorithms.
Discrete System Simulation Principles of simulation of discrete, event-oriented systems. Model construction, simulation and validation. Distributed and parallel simulation techniques. Basic statistical analysis of simulation inputs and outputs. Use of C, an object-oriented language, and S, a statistical analysis package.
Prior familiarity with the concepts of probability and statistics desirable. Computer System Performance Emphasizes the use of analytic models as tools for evaluating the performance of centralized, distributed, and parallel computer systems. Principles Of Database Systems The relational data model: Machine Learning Explores methods for designing systems that learn from data and improve with experience.
Supervised learning and predictive modeling; decision trees, rule induction, nearest neighbors, Bayesian methods, neural networks, support vector machines, and model ensembles. Machine Learning for Big Data Machine Learning and statistical techniques for analyzing datasets of massive size and dimensionality. Representations include regularized linear models, graphical models, matrix factorization, sparsity, clustering, and latent factor models.
Algorithms include sketching, random projections, hashing, fast nearest-neighbors, large-scale online learning, and parallel Map-reduce, GraphLab. This course is cross-listed as STAT Computer Systems Architecture Notations for computer systems. Processor design single chip, look-ahead, pipelined, data flow. Memory hierarchy organization and management virtual memory and caches. High-performance Computer Architectures Algorithm design, software techniques, computer organizations for high-performance computing systems.
VLSI complexity for parallel algorithms, compiling techniques for parallel and vector machines, large MIMD machines, interconnection networks, reconfigurable systems, memory hierarchies in multiprocessors, algorithmically specialized processors, data flow architectures.
Computer Systems Explores computer system design, implementation, and evaluation. Covers principles, techniques, and examples related to the construction of computer systems, including concepts that span network systems, operating systems, web servers, parallel computing, and databases.
Operating Systems Operating system design and construction techniques. Concurrent programming, operating system kernels, correctness, deadlock, protection, transaction processing, design methodologies, comparative structure of different kinds of operating systems, and other topics.
Distributed And Parallel Systems Principles, techniques, and examples related to the design, implementation, and analysis of distributed and parallel computer systems. Real-time Systems Design and construction of software for real-time computer systems. Requirements and specification methods. Scheduling algorithms and timing analysis. Computer Graphics Introduction to image synthesis and computer modeling, emphasizing the underlying theory required for undertaking computer graphics research.
Topics include color theory, image processing, affine and projective geometry, hidden-surface determination, photorealistic image synthesis, advanced curve and surface design, dynamics, realistic character animation.
CSE major, solid knowledge of linear algebra. Topics vary from year to year but typically include advanced aspects of image synthesis, animation, and 3D photography. Computer Communications And Networks Fundamentals of data transmission: Organization of computer networks. Examples of network implementations. Computer Security And Privacy Examines the fundamental of computer security including: Principles Of Digital Systems Design Principles of logic design, combinational and sequential circuits, minimization techniques, structured design methods, CMOS technology, complementary and ratioed gates, delay estimation and performance analysis, arithmetic circuits, memories, clocking methodologies, synthesis and simulation tools, VLSI processor architecture.
CSE major and basic knowledge of logic design. CSE or permission of instructor. Probabilistic Robotics This course introduces various techniques for Bayesian state estimation and its application to problems such as robot localization, mapping, and manipulation. The course will also provide a problem-oriented introduction to relevant machine learning and computer vision techniques.
Artificial Intelligence Intensive introduction to artificial intelligence: Artificial Intelligence II Advanced topics in artificial intelligence. Subjects include planning, natural language understanding, qualitative physics, machine learning, and formal models of time and action. Students are required to do projects. Computer Vision Overview of computer vision, emphasizing the middle ground between image processing and artificial intelligence.
Image formation, preattentive image processing, boundary and region representations, and case studies of vision architectures. Solid knowledge of linear algebra, good programming skills, CSE or E E major or permission of instructor. Special Topics In Computer Vision Topics vary and may include vision for graphics, probabilistic vision and learning, medical imaging, content-based image and video retrieval, robot vision, or 3D object recognition.
Topics from deterministic and stochastic optimal control, reinforcement learning and dynamic programming, numerical optimization in the context of control, and robotics. Parallel Computation In Image Processing Parallel architectures, algorithms, and languages for image processing.
Cellular array, pipelined and pyramid machines, instruction sets, and design issues. Parallel implementations of filtering, edge detection, segmentation, shape, stereo, motion, relaxation algorithms, multiresolution methods, and iconic-to-symbolic transforms.
Students write and debug programs for parallel computers. Software Development for Data Scientists Provides students outside of CSE with a practical knowledge of software development that is sufficient to do graduate work in their discipline. Modules include Python basics, software version control, software design, and using Python for machine learning and visualization.
Performance Analysis Broad introduction to computer system performance evaluation techniques and their application. Molecular Biology as a Computational Science Molecular biology for computer science students interested in computational research in the Life Sciences, such as bioinformatics and bioengineering. Special Topics In Computer Science Entrepreneurship This course is about entrepreneurship and specifically about starting, growing, managing, leading, and ultimately exiting a new venture.
Constraint Programming Design, implementation, and use of constraint programming languages. It is open to all graduate students in the computer, biological, and mathematical sciences. See the web pages at left for an outline of activities this quarter and in the recent past.
Change Seminar Change Seminar. Educational Technology Research seminar on educational issues. Special Topics in Embedded Systems Seminar or project course where students investigate different aspects of embedded systems.
Topics change with each offering. The objective is to expose students to the different nature of task-specific computing devices and their communication methods as opposed to general-purpose computing systems and networks. Architecture Lunch A reading seminar in which we discuss current research published in the key architecture conferences or advanced subtopics in computer architecture.
Computer networks seminar Computer networks seminar. Software Engineering Seminar A seminar for those interested in general topics in software engineering.
It's run by David Notkin and is historically quite informal. Neural Computation Seminar Neural computation and learning. Supervised and unsupervised learning Reinforcement learning and imitation learning Bayesian inference and relationship to neural networks Recurrent and hierarchical networks Applications in computer vision, robotics, and brain-computer interfaces.
Programming Systems Programming Systems. Database Seminar Database seminar.