Boston University | Center for Computational Science
HomeNews and EventsResearchEducationPeopleSeminarsFacilitiesContact Us

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 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 Mechanical Engineering

ME/SE 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 Mechanical Engineering

ME 719 Computational Problem Solving

Department of Mechanical Engineering

ME/SE/EC 724 Advanced 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 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 7/22/2009. Please send comments to Cheryl Endicott

    Home

    copyright © 2006, Center for Computational Science | Boston University , MA, 02215