| Current
Computational Courses and Tutorials
The initial set of
courses from which students can
choose the two computational courses required
to fulfill the certificate's requirements are
the following:
Department
of Aerospace and Mechanical Engineering
ENG AM 504 Numerical Methods for Engineers
Prereq: graduate standing
or consent of instructor.
Survey of numerical methods with examples selected
from Aerospace and Mechanical Engineering. Numerical
solution of systems of linear and non-linear
algebraic equations, interpolation and extrapolation,
computation of eigenvalues and eigenvectors,
numerical integration, techniques for numerical
solution of ordinary differential equations
and partial differential equations. Required
projects involve extensive student use of computers.
(4 credits)
ENG AM 702 Computational
Fluid Dynamics
Prereq: ENG AM 504, AM 542, AM 543.
Numerical techniques for solving the Navier-Stokes
and related equations. Topics are selected from
the following list, although the emphasis may
shift from year to year: boundary integral methods
for potential and Stokes flows; free surface
flow computations; panel methods; finite difference,
finite element and finite volume methods; spectral
and pseudospectral methods; vortex methods;
lattice-gas and lattice-Boltzmann techniques;
numerical grid generation. (4 credits)
Department
of Astronomy
GRS AS 751 Galactic Astronomy
and the Interstellar Medium
Prereq: GRS AS 712, 713, 726 or
consent of instructor. Physical
processes in interstellar gas. Gaseous nebulae.
Star formation. Neutral hydrogen and galactic
structure. Molecular clouds, ionized hydrogen
regions, planetary nebulae, supernova remnants.
Dust and extinction. Cosmic rays and the galactic
magnetic field. Bania, Clemens and Jackson.
(4 credits)
Department
of Chemistry
GRS CH 651, 652 Molecular Quantum Mechanics
Prereq: CAS CH 351, CH 352 or
equivalent. Suggested coreq: GRS
CH 654. Introduction to quantum
theory, atomic and molecular structure, spectroscopy.
The chemical bond; Born-Oppenheimer approximation;
electronic, vibrational and rotational motion
in molecules. NMR, EST, microwave, IR, raman,
visible and UV spectroscopy. Computational ab
initio methods for analyzing molecular structure
and spectroscopy. Three hours lecture, two hours
discussion. Coker, Ziegler. (4 credits,
1st and 2nd semester)
GRS CH 655 Equilibrium
Statistical Mechanics
Prereq: CAS CH 352 or
equivalent. Fundamental principles,
including ensemble theory, Fermi-Dirac, Bose-Einstein
and classical statistics; phase transitions;
classical applications, including the Mayer
expansion, density expansion of the equation
of state and Debye-Huckel theory; time-dependent
phenomena, including irreversible thermodynamics;
scattering, spectroscopy,and time-correlation
functions. Introduction to numerical methods
of differentiation, integration, linear algebra,
and solution of differential equations. Computational
methods for Molecular Dynamics and Monte Carlo
simulation of many-body systems. Three hours
lecture. Straub. (4 credits, 1st
semester)
|
GRS CH 751 Advanced Topics
in Physical Chemistry
Prereq: GRS CH 652.
Current topics in theoretical, computational
and experimental physical chemistry. Coker,
Hoffman, Keyes, Prock, Straub. (4
credits, either semester)
|
GRS CH 752 Advanced Topics
in Chemical Physics
Prereq: GRS CH 652.
Current topics in theoretical, computational and
experimental chemical physics. Clarke, Coker,
Dill, Keyes, Straub, Ziegler. (4 credits,
either semester)
|
Department of Cognitive and Neural Systems
CAS CN 500 Computational Methods in
Cognitive and Neural Systems
Prereq: one year of calculus
or consent of instructor.
Introduction to mathematical methods and computer
simulation for modeling cognitive and neural
systems. Topics include; computer simulation
methods, control theory, difference and differential
equations, digital signal processing, image
processing, optimization and statistics. Selected
readings from current literature emphasize theory
and applications relevant to the study of cognitive
and neural systems. Rucci. (4 credits,
1st semester)
|
CAS CN 510 Principles
and Methods of Cognitive and Neural Modeling
I
Prereq: one year of calculus
and consent of instructor.
Explores psychological, biological, mathematical,
and computational foundations of behaviorial
and brain modeling. Topics include organizational
principles, mechanisms, local circuits, network
architectures, cooperative and competitive nonlinear
feedback systems, associative learning systems,
and self-organizing, code-compression systems.
The adaptive resonance theory model unifires
many course themes. CAS CN 510
and CN 520 may be taken concurrently.
Guenther. (4 credits, 1st semester)
|
CAS CN 520 Principles
and Methods of Cognitive and Neural Modeling
II
Prereq: one semester of linear algebra
and consent of instructor.
Analyzes three main traditions in models of
learning: unsupervised (self-organized) learning,
supervised learning (learning with a teacher),
and reinforced learning. Architectures studied
include adaptive filters, back propagation,
competitive learning, self-organizing feature
maps, gradient descent procedures, Boltzmann
machines, simulated annealing, neocognitron
and gated dipoles. CAS CN 510 and CN 520 may
be taken concurrently. Shinn-Cunningham. (4
credits, 1st semester)
|
CAS CN 550 Neural and
Computational Models of Recognition, Memory
and Attention
Prereq: CAS CN 510 or
consent of instructor. Develops
neural-network models of how internal representations
of snesory events and cognitive hypotheses are
learned and remembered as well as of how such
representations enable recognition and recall
of these events to occur. Various neural and
statistical pattern-recognition models are analyzed.
Special attention is given to stable self-organization
of pattern-recognition and recall codes by Adaptive
Resonance Theory (ART) models. Mathematical
techniques and definitions to support fluent
access to the neural network and pattern-recognition
literature are developed throughout the course.
Experimental data and theoretical predictions
from cognitive psychology, neuropsychology,
and neurophysiology of normal and abnormal individuals
are also analyzed. Coursework emphasizes skill
development, including writing, computational
analysis, teamwork and verbal communication.
Carpenter. (4 credits, 2nd semester)
|
GRS CN 700 Computational and Mathematical Methods
in Neural Modeling
Prereq: consent of instructor.
Introduction to advanced computational topics
used in quantitative modeling. Techniques from
signal processing, probability, statistics, vector
quantization, optimal control, and ordinary and
partial differential equations. Theory, simulations
and techniques illustrated with neural networks
and other behaviorial and biological models. Cohen.
(4 credits, 2nd semester)
|
|
Department of Computer Science
CAS CS 511 Object-Oriented Software
Principles
Prereq: CAS CS 320, CS 411 or
consent of instructor. Specification,
programming, analysis of large-scale, reliable
and reusable JAVA software using object-oriented
design principles. Topics may include object-oriented
programming, object models, memory models, inheritance,
exceptions, namespaces, data abstraction, design
against failure, design patterns and reasoning
about objects. Kfoury. (4 credits,
2nd semester)
|
CAS CS 520 Programming
Languages
Prereq: CAS CS 320, CS 332
or consent of instructor.
Concepts of programming languages: data, storage,
control and definition structures; concurrent
and distributed programming; functional and logic
programming. XI. (4 credits, 1st semester)
|
CAS CS 530 Analysis of Algorithms
Prereq: CAS CS 330 or
consent of instructor.
Studies the design and efficiency of algorithms
in several areas of computer science. Topics
may be chosen from : graph algorithms, sorting
and searching, NP-complete problems, pattern-matching,
parallel algorithms and dynamic programming.
Gacs. (4 credits, 2nd semester)
|
CAS CS 560 Introduction to Database Systems
Prereq: CAS CS 320
and CS 350 or consent
of instructor. Examines data models:
entity-relationship, hierarchical, network and
mainly relational; commercial relational languages,
relational database design, file organization,
indexing and hashing, query optimization, transaction
processing, concurrency and recovery techniques,
integrity, security. Kollios. (4
credits, 2nd semester) |
CAS CS 562 Advanced Database Applications
Research issues in the design and implementation
of modern database systems. Spatial, temporal
and spatiotemporal index structures. Indexing
methods for image and multimedia databases and
data warehouses. New data anlaysis techniques
for large databases, clustering and rule discovery
for very large datasets. Kollios.
(4 credits, 1st semester) |
Department of Electrical and Computer Engineering
ENG EK 521 Parallel Computation for
Engineering
Prereq: CAS MA 226,
CAS PY212 and ENG
EK 420 or consent
of instructor. Methods of parallel
computing for science and engineering applications
are presented through lectures and programming
exercises drawn from continuum mechanics, diffusive
transport, magnetic materials and molecular
modeling. Given the appropriate equations of
motion, each student is guided to develop parallel
algorithms, design simulation software and analyze
the resulting data using proper statistical
and graphical analysis methods. In addition
to the weekly laboratories, each student completes
a term project. (4 credits)
|
ENG SC 513 Computer Architecture
Prereq: ENG SC 312.
The concepts of computer architecture from a quantitative
approach. Instruction set design with examples
from both RISC and CISC architectures. Processor
implementation techniques and microprogramming.
Pipelining and methods to cope with pipeline hazards.
The memory hierarchy: cache and virtual memory.
Parallel and vector architectures, future directions
and examples of highly parallel computers. (4
credits, 1st semester)
|
ENG SC 713 Parallel Computer Architecture
Prereq: ENG SC 513.
Basic problems of parallel processing and how
they are addressed by current parallel computers.
Topics include characteristics of parallel applications,
snoop-and directory-basesd cache coherency protocols,
interconnection network design, scalable systems
and hardware-software tradeoffs. Meets with CAS
CS 551. (4 credits)
|
ENG SC 719 Statistical Pattern Recognition
Prereq: EK500, SC 381 or equivalent.
This course discusses the tools of statistical
pattern recognition and machine learning. Topics
include Bayesian decision theory, maximum likelihood
and Bayesian estimation, nonparametric density
estimation and classifiers, classification and
regression trees, nonparametric statistics, unsupervised
learning, clustering and feature aggregation.
The course requires a computation project involving
application of different pattern recognition techniques
to large classification problems.(4
credits, 1st semester every two years.)
|
Department of Manufacturing Engineering
ENG MN 524 Optimization Theory and Methods
Prereq: MN409 or
consent of instructor. Introduction
to optimization problems and algorithms emphasizing
problem formulation, basic methodologies and
the underlying mathematical structures. Covers
the classical theory of linear and nonlinear
optimization as well as recent advances in the
field. Topics include: modeling issues, simplex
method, duality theory, sensitivity analysis,
large scale optimization, integer programming,
interior-point methods, nonlinear programming
optimality conditions, Lagrange multipliers,
gradient methods, and conjugate direction methods.
Applications of the theory and techniques developed
in the course will be considered and a few case
studies will be analyzed. In addition to extensive
paradigms from production planning and scheduling
in manufacturing systems other illustrative
applications include: fleet management, air
traffic flow management, optimal routing in
communication networks, and optimal portfolio
selection. Instructor, Yannis Paschalidis. Class
meets Tue, Thu from 2:00-4:00pm at PHO 201.
Course
information sheet. |
Department of Manufacturing Engineering
ENG MN 714 Advanced Stochastic Modeling
and Simulation
Prereq: ENG EK 500 or
equivalent, knowledge of stochastic
processes or consent
of the instructor. Introduction
to Markov chains, point processes, diffusion processes
as models of stochastic systems of practical interest.
The course focuses on numerical and simulation
methods for performance evaluation, optimization
and control of such systems. (4 credits)
|
Department of Mathematics & Statistics
CAS MA 539 Methods of Scientific Computing
Prereq: CAS MA 225, MA 242, CS 330
or consent of instructor.
An introductory survey of topics, including computational
linear algebra, numerical integration and solution
of differential equations, solution of linear
equations, optimization, pseudorandom number generation
and methods of stochastic simulation (i.e. Monte
Carlo methods.) Kolaczyk. Meets with CAS
CS 539. (4 credits,
1st semester) |
Department of Physics
CAS
PY 502 Computational Physics
Prereq: consent of instructor.
Fundamental methods of computational physics and
applications, numerical algorithms, linear algebra,
differential equations, computer simulation, vectorization,
parallelism and optimization. Examples and projects
on scientific applications. Rebbi. (4
credits, 1st semester) |
GRS PY 621 Advanced Scientific Computing in Physics
Introduces advanced computational techniques for
research problems in physics, with emphasis on
computationally intensive applications in a massively
parallel supercomputing environment. Rebbi (4
credits, 2nd semester) |
This list of courses will
be reviewed and modified by the board, as appropriate.
|
The initial set of tutorials
offered by SCV from which students can choose
the tutorials required to achieve competence in
basic computational techniques and in the use
of the University's advanced computing facilities
are the following: |
Introduction to Image Files and Color Output
Introduction to Scientific Visualization
Tools
Using IDL to Manipulate and Visualize Scientific
Data
Introduction to Scientific Computing on
the IBM SP and Regatta (not yet available,
but expected in the near future)
Multiprocessing with Fortran 90
Multiprocessing by Message Passing MPI (also
available as an Alliance tutorial)
Introduction to MATLAB
Introduction to OpenMP (requires audio)(also
available as an Alliance tutorial)
Introduction to 3-D Modeling and Animation
using Maya
|
This list of tutorials
will be reveiwed and modified by the board, as
appropriate |
Page last updated 01/23/07. Please send comments
to Cheryl
Endicott |
Home
|
