CSCE Object-Oriented Programming, Development and Software Engineering. Credits 3. 3 Lecture Hours. Teaches Object-Oriented Programming in C++; software engineering techniques presented to teach how to build high quality software; semester project gives quasi-real-world experience with issues such as requirements capture and object-orient development Lead. Motivate. Succeed. You pour your heart and soul into the work you do; now harness that drive to make an even bigger impact. The Fischler College of Education and School of Criminal Justice's degree programs help you hone your career expertise, with the curriculum flexibility, support, and personalized coaching that makes NSU unique STAT Statistical Aspect of Machine Learning II: Modern Techniques. Credits 3. 3 Lecture Hours. Second course in statistical machine learning; recursive partition and tree-based methods, artificial neural networks, support vector machines, reproducing kernels, committee machines, latent variable methods, component analysis, nonlinear dimensionality reduction and manifold
CSCE - Computer Sci. & Engr. < Texas A&M Catalogs < Texas A&M University, College Station, TX
Survey of the C and Java programming languages, including principles of procedural and object-oriented languages; multi-disciplinary applications including business, dissertation and itde, Internet and engineering problems.
Prerequisite: Graduate classification. CSCE Object-Oriented Programming, Development and Software Engineering. Prerequisites: CSCE or dissertation and itde of instructor; graduate classification. Introduction to the concepts and design methodologies of database systems for non-computer science majors; emphasis on E. Codd's relational model with hands-on design application. Only one of the following will satisfy the requirements for a degree: CSCE or CSCE Prerequisites: CSCE ; graduate classification.
Study in the design space of programming languages, dissertation and itde, covering language processing, formalisms to describe semantics of programming languages, important concepts found in current programming languages, and programming paradigms.
Advanced dissertation and itde in compiler writing; parser generators and compiler-compilers; dynamic storage dissertation and itde scope resolution; data flow analysis and code optimization, dissertation and itde.
Prerequisite: CSCE Development of advanced concepts in software engineering; software development environments as a mechanism for enhancing productivity and software quality; the classification, evaluation and selection of methodologies for environments; rapid prototyping and reusability concepts; artificial intelligence techniques applied to software engineering.
Prerequisite: CSCE or approval of instructor. Database modeling techniques; expressiveness in query languages including knowledge representation; manipulation languages data models; physical data organization; relational database design theory; query processing; transaction management and recovery; distributed data management.
Prerequisite: CSCE or approval of Instructor. Prerequisites: CSCE or CSCE ; CSCE CSCE Applied Networks and Distributed Processing. Fundamentals, including network design and protocol analysis, in the context of computer communications; mixes fundamentals with both programming and pragmatic views of engineering issues; it includes network architecture as well as principles of network engineering; focus is on applying principles of layered architecture to analyzing real networks; lab exercises focus on protocol understanding and programming; knowledge of UNIX and C programming helpful, but not required.
Analysis of algorithms in computer operating systems; sequencing and control algorithms supporting concurrent processes; scheduling algorithms to minimize execution times and mean flow times; algorithms for allocating tasks to processors; allocation of memory virtual and real ; direct access device schedules; auxiliary and buffer storage models.
Prerequisite: CSCE or CSCE Reviews of von Neumann architecture and its limitations; parallel computer structures and concurrent computation; pipeline computers and vectorization methods; array processors, multiprocessor architectures and programming; dataflow computers. CSCE Introduction to Hardware Dissertation and itde Verification. Introduction to hardware functional verification; case studies on verification in integrated circuit design; introduction to industry best practices; introduction to logic functional verification.
CSCE Co-Design of Embedded Systems CODES. Co-design methodologies of hardware-software systems; models of computation MOCsystem specification, co-simulation, dissertation and itde, synthesis, and verification; hardware-software implementation; core-based systems and interfaces, performance analysis and optimization; system on chip, power aware design. Prerequisites: CSCE or equivalent, CSCE and dissertation and itde classification.
Computer network concepts including network architecture, layering, protocols, packet switching and virtual circuits; performance evaluation and design considerations for local area networks; packet distributed networks; satellite networks.
Design and analysis of algorithms for solving geometrical problems; includes convex hull problems, Voronoi diagrams, range searching and proximity problems. The generic programming approach to design and systematic classification of software components, techniques for achieving correctness, efficiency, and generality of algorithms, dissertation and itde, data structures, and memory management, methods of structuring a library of generic software components for maximum usability are practiced in a significant design and implementation project.
Analysis, implementation, and comparison of sketch recognition algorithms, including feature-based, vision-based, geometrical, timing-based, and path-based recognition algorithms; methods for combining these recognition methods for greater accuracy, dissertation and itde, using known AI techniques.
Basic concepts and dissertation and itde of artificial intelligence; Heuristic search procedures for general graphs; game playing strategies; resolution and rule based deduction systems; knowledge representation; reasoning dissertation and itde uncertainty, dissertation and itde.
CSCE Parallel Algorithm Design and Analysis. Design of algorithms for use on highly parallel machines; area-time complexity of problems and general lower bound theory; application of these concepts to artificial intelligence, computer vision and VLSI design automation.
Formal models of computation such as pushdown automata; Turing machines and recursive functions; unsolvability results; complexity of solvable results. Introduction to computational biology; formulations of biology problems as computational problems; computational approaches to solve problems in genomics and proteomics. Prerequisite: Graduate classification or approval of instructor.
Concrete algorithm design and analysis; abstract models to analyze the complexity of problems; NP-Completeness; approximation and probabilistic algorithms. Prerequisites: ECEN or equivalent dissertation and itde approval of instructor; basic knowledge of signals and systems, linear algebra, probability and statistics; programming experience in a high-level language is required.
On the design and implementation of Intelligent Agents and coordination mechanisms among multiple agents, ranging from theoretical principles to practical methods for implementation. Machine learning is the study of self-modifying computer systems that can acquire new knowledge and improve their own performance; survey machine learning techniques, which include induction from examples, conceptual clustering, explanation-based learning, exemplar learning and analogy, discovery and genetic algorithms.
Intersection of artificial intelligence and computer-human interaction; emphasis on designing and evaluating systems that learn about and adapt to their users, tasks, and environments. Prerequisites: Graduate classification and approval of instructor.
Introduction and survey of artificial intelligence methods for mobile robots ground, aerial, dissertation and itde, or marine for science and engineering majors; theory and practice of unmanned systems, focusing on biological and cognitive principles which differ from control theory formulations. Basic concepts in neural computing; functional equivalence and convergence properties of neural network models; associative memory models; associative, competitive and adaptive resonance models of adaptation and learning; selective applications of neural networks to vision, speech, motor control and planning; neural network modeling environments.
Prerequisites: MATH and MATH or approval of instructor, dissertation and itde. Deterministic, non-deterministic, alternating and probabilistic computations; reducibilities; P, NP and other complexity classes; abstract complexity; time, space and parallel complexity; and relativized computation. CSCE Natural Language Processing: Foundations and Techniques. Focus on teaching Natural Language Processing NLP fundamentals including language models, automatic syntactic processing and semantic understanding; introduction to major NLP applications including information extraction, machine translation, text summarization, dialogue systems and sentiment analysis.
Introduces the basics of fuzzy logic and its role in developing intelligent systems; topics include fuzzy set theory, fuzzy rule inference, fuzzy logic in control, fuzzy pattern recognition, dissertation and itde, neural fuzzy systems and fuzzy model identification using genetic algorithms.
Introduction to the design and analysis of quantum algorithms; basic principles of the quantum circuit model; gives a gentle introduction to basic quantum algorithms; dissertation and itde recent results in quantum information processing. CSCE Seminar in Intelligent Systems and Robotics. Problems, methods and recent developments in intelligent systems and robotics.
May be taken at multiple times for credit as content varies. Prerequisite: Approval of instructor. The architecture of the mammalian cerebral cortex; its modular organization and its network for distributed and parallel processing; cortical networks in perception and memory; neuronal microstructure and dynamical simulation of cortical networks; the cortical network as a proven paradigm for the design of cognitive machines.
Dissertation and itde CSCE or CSCE and CSCE and graduate classification. Prerequisites: CSCE and CSCE or equivalent. Tools and techniques dissertation and itde generation, handling and analysis of two dimensional digital images; image representation and storage; display, media conversion, painting and drawing; warping; color space operations, enhancement, filtering and manipulation.
Principles of dissertation and itde synthesis from 3-D scene descriptions; includes local and global illumination, shading, shadow determination, hidden surface elimination, texturing, raster graphics algorithms, transformations and projects. Mathematical and artistic principles of 3-D modeling and sculpting; includes proportions, skeletal foundation, expression and posture, line of action; curves, surfaces and volumes, dissertation and itde, interpolation and approximation, parametric and rational parametric polynomials, constructive solid geometry, and implicit representations.
Physical simulation as used in choreography, geometric modeling, and the creation of special effects in computer graphics; a variety of problems and techniques are explored which may include particle-methods, modeling and dissertation and itde of flexible materials, kinematics and constraint systems. Theory and practice of virtual reality VR ; interactive 3D virtual environments, immersive technology, perceptual realism, and embodied interaction experience; overview dissertation and itde VR with topics including input devices, output devices, 3D interaction techniques, augmented reality, the role of realism in VR, navigation techniques, design guidelines, and evaluation methods; hands-on experience designing VR experiences emphasizing application, demonstration, or research purposes.
Introduction to the compilation mechanism to generate executable files and raw binary codes from sources codes; the executable file formats for an operation system to run the binary code; disassembly algorithms and control graph analysis; static and dynamic analyses; case studies on code obfuscation, codebreaking and malware analysis. Prerequisites: CSCE or approval of instructor. Classical and modern techniques for the computational solution of problems of the type that traditionally arise in the natural sciences and engineering; introductions to number representation and errors, locating roots of equations, dissertation and itde, interpolation, numerical integration, linear algebraic systems, spline approximations, initial-value problems for ordinary differential equations and finite-difference methods for partial differential equations.
Prerequisite: CSCE or MATH ; graduate classification. Principles of high-performance scientific computing systems, vectorization, programming on supercomputers, numerical methods for supercomputers, performance measuring of supercomputers, multitasking. A foundation course in human centered systems and information; understanding and conceptualizing interaction; design and prototyping methodologies; evaluation frameworks; visual design using color, space, layering, and media; information structuring and visualization; animation and games; individual and team programming projects.
Prerequisite: Graduate classification or CSCE or or approval of instructor. This class investigates the potential and realized impact of computers in the design of new media, explores the variety of relationships between authors and readers of interactive materials, and explores the influence of media design and content expressed.
Numerical simulation of problems in Earth Sciences and Petroleum Engineering using high performance computing HPC ; development of a parallel reservoir simulator. Introduction to randomized algorithms; selected tools and techniques from probability theory and game theory are reviewed, with a view towards algorithmic applications; the main focus is a thorough discussion dissertation and itde the main paradigms, techniques, and tools in the design and analysis of randomized algorithms; a detailed analysis of numerous algorithms illustrates the abstract concepts and techniques.
A unified treatment of parallel and distributed numerical algorithms; parallel and distributed computation models, parallel computation of arithmetic expressions; fast algorithms for numerical linear algebra, partial differential equations and nonlinear optimization.
Prerequisites: CSCE ; MATH Techniques in matrix computation including elimination methods, matrix decomposition, generalized inverses, dissertation and itde, orthogonalization and least-squares, eigenvalue problems and singular value decomposition, iterative methods and error analysis. Prerequisite: MATH or equivalent or CSCE or equivalent. VLSI design systems and their levels of abstracting; algorithms for general VLSI design and implementation; computer aided design tools and principles; physical and logical models.
Principles and practices of distributed processing; protocols, remote procedure calls; file sharing; reliable system design; load balancing; distributed database systems; protection and security; implementation. Prerequisite: CSCE and CSCE or CSCE Taxonomy of real-time computer systems; scheduling algorithms for static and dynamic real-time tasks; hard real-time communications protocols; programming languages and environments for real-time systems; case studies of real-time operating systems.
Prerequisites: CSCEand CSCE or CSCEor approval of instructor, dissertation and itde. Wireless and mobile systems; wireless communication fundamentals; wireless medium access control design; transmission scheduling; network and transport protocols over wireless design, simulation and evaluation; wireless capacity; telecommunication systems; vehicular, adhoc, and sensor network systems; wireless security; mobile applications.
Prerequisite: CSCE or CSCE or approval of instructor. Security aspects of various network protocols including investigation and tool development using "live" machines and networks. Introduction to methods for the analysis, classification and dissertation and itde of high dimensional data in Computer Science applications; includes density and parameter estimation, linear feature extraction, feature subset selection, clustering, dissertation and itde, Bayesian and geometric classifiers, non-linear dimensionality reduction methods from statistical dissertation and itde theory and spectral graph theory, Hidden Markov models, and ensemble learning.
Prerequisites: MATHMATH or equivalent, and graduate classification. Problems, methods and recent developments in human-centered computing and information. May be repeated for credit as content varies. Prerequisites: Graduate classification. Introduction to fundamental algorithmic results in distributed computing systems; leader dissertation and itde, mutual exclusion, consensus, logical time and causality, distributed snapshots, algorithmic fault tolerance, shared memory, clock synchronization.
Prerequisites: CSCE or equivalent or approval of instructor. Combinatorial theory of polytopes as a dissertation and itde for the solution of combinatorial optimization problems; applications to max flow, matching and matroids; geometric interpretation of the results indicating the profound role that polyhedral combinatorics play in the design and complexity of approximation algorithms, dissertation and itde.
Representation, storage, and access to very large multimedia document collections; fundamental data structures and algorithms of information storage and retrieval systems; techniques to design and evaluate complete retrieval systems, including cover of algorithms for indexing, compressing, and querying very large collections, dissertation and itde. Prerequisites: CSCE or CSCE or approval of instructor; graduate classification. Comprehensive coverage of Computer-human Interaction CHI including history, importance, design theories and future direction; modeling computer users and interfaces, empirical techniques for dissertation and itde analysis and interface design, and styles of interaction.
CSCE Computer Supported Collaborative Work, dissertation and itde. Covers design, dissertation and itde, implementation and use of technical systems that support people working cooperatively; draws from the research area of Computer Supported Cooperative Dissertation and itde CSCW and includes current theoretical, practical, dissertation and itde, technical and social issues in CSCW and future directions of the field.
Surveys current research and practice in Digital Libraries, which seek to provide intellectual access to large-scale, distributed digital information repositories; current readings from the dissertation and itde literature which covers the breadth of this interdisciplinary area of study.
Prerequisite: Graduate classification in computer science.
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, time: 8:50STAT - Statistics < Texas A&M Catalogs < Texas A&M University, College Station, TX
STAT Statistical Aspect of Machine Learning II: Modern Techniques. Credits 3. 3 Lecture Hours. Second course in statistical machine learning; recursive partition and tree-based methods, artificial neural networks, support vector machines, reproducing kernels, committee machines, latent variable methods, component analysis, nonlinear dimensionality reduction and manifold CSCE Object-Oriented Programming, Development and Software Engineering. Credits 3. 3 Lecture Hours. Teaches Object-Oriented Programming in C++; software engineering techniques presented to teach how to build high quality software; semester project gives quasi-real-world experience with issues such as requirements capture and object-orient development Lead. Motivate. Succeed. You pour your heart and soul into the work you do; now harness that drive to make an even bigger impact. The Fischler College of Education and School of Criminal Justice's degree programs help you hone your career expertise, with the curriculum flexibility, support, and personalized coaching that makes NSU unique
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