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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.


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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.

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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.

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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.

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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.

A Review of Some Subtleties of Practical Relevance for Time-Delay Systems of Neutral Type

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.

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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.

Convex Optimization Basics of convex analysis: Get Advances in Systems Science: Proceedings of the PDF. This ebook examines discrete dynamical platforms with memory—nonlinear structures that exist largely in organic organisms and monetary and monetary corporations, and time-delay platforms that may be discretized into the memorized, discrete dynamical platforms.