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Computer Science

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Code for Neural Networks and Reinforcement Learning (Post-Graduate)  
This site contains code for use in neural network research. The code illustrates how to train neural networks using MATLAB and C. There is code that illustrates how to initialize and train a recurrent neural network using Williams and Zipser's Real-Time Recurrent Learning algorithm. In addition, there are files that can be used for training feedforward neural networks with a single hidden layer using error backpropagation with early stopping using MATLAB and Octave.

Professor Name: Charles W. Anderson
Department: Computer Science
University: Colorado State University

Submitted: Aug 22, 2007
The AVL Page  
This page is here to distribute my AVL tree library and my AVL deletion algorithm. I have written the algorithm used in the deletion routine, since it seems that there is no reference book available today that gives a fully worked out algorithm for this.
Submitted: Jul 31, 1999
Scientific Computing  
Description: Aim of this course: This course intends to teach mathematical methods for the numerical solution of large scale problems. In particular, methods for the solution of large systems of linear(ized) equations.
Target audience: Introductory undergraduate (1st or 2nd year)
Institution: Technische Universiteit Eindhoven
Materials available: Problem sets or projects, Course outline or syllabus, Textbook recommendations, Primers or tutorials, Downloadable code or data files
Products: MATLAB
Course material created by Dr.ir. M.J.H. Anthonissen
Submitted: Oct 16, 2008
Computational Science and Engineering I  
Description: This course provides a review of linear algebra, including applications to networks, structures, and estimation. Also covered are: differential equations of equilibrium; Laplace's equation and potential flow; boundary-value problems; minimum principles and calculus of variations; Fourier series; discrete Fourier transform; convolution; and applications.
Course material created by Professor Gilbert Strang.
Target audience: Graduate
Institution: Massachusetts Institute of Technology
Materials available: Problem sets or projects, Course outline or syllabus, Textbook recommendations, Downloadable code or data files
Products: MATLAB

Submitted: Jul 30, 2008
Optimization Models in Computational Biology  
Description: This course will introduce a series of optimization models that find applications to various problems in bioinformatics and computational biology. The basics of the following topics will be covered: dynamic programming; graph algorithms (paths and flows); clustering and trees; linear, non-linear, and integer programming (including convex polytopes); certain probabilistic models; and very limited algebraic statistics. The applications of these optimization models to bioinformatics and computational biology will be illustrated by studying problems such as sequence motif search, DNA sequence alignment (including parametric sequence alignment), recombinations and other related phylogenetic problems, protein sequencing, and protein structure prediction (including side-chain positioning, scoring functions for threading, molecular dynamics etc.).
Course material created by Professor Bala Krishnamoorthy.
Target audience: Graduate
Institution: Washington State University
Materials available: Problem sets or projects, Course outline or syllabus, Textbook recommendations
Products: MATLAB

Submitted: Jul 30, 2008
Introduction to Applied Scientific Computing with MATLAB  
Description: This course will introduce the basic syntax and features of MATLAB and will develop the background necessary for more specialized courses. Topics covered include basic MATLAB programming and vectorized operations, data input/output, and simple visualization. The course will emphasize applied issues such as managing large data sets, simulation, and visualization, but will also introduce fundamental ideas in scientific computing such as floating point arithmetic and algorithm efficiency.
Course material created by Professor Andrew J. Pershing.
Target audience: Advanced undergraduate (3rd or 4th year)
Institution: Cornell University
Materials available: Presentations, Course outline or syllabus, Downloadable code or data files
Products: MATLAB

Submitted: Jul 30, 2008
Introduction to Computer Techniques in Physics  
Description: This course provides an introduction to the use of computers in physics based on examples from mechanics and astronomy. Topics covered include computers and networking; numerical analysis in algebraic systems and approximations; numerical analysis in differential and integral calculus; data analysis and visualization; and scientific computing tools.
Course material created by Professor J.C Evans.
Target audience: Advanced undergraduate (3rd or 4th year)
Institution: George Mason University
Materials available: Problem sets or projects, Course outline or syllabus, Textbook recommendations, Downloadable code or data files
Products: MATLAB

Submitted: Jul 22, 2008
Numerical Algorithms  
Description: This course gives an introduction into a subject connecting applied mathematics and computer science - numerical methods. This course aims to provide a solid background on numerical techniques for solution of linear problems. Topics covered include: linear systems of equations, Gaussian elimination, Cholesky factorization, conditioning and stability, QR factorization, least squares problems, singular value decomposition, eigenvalue problems, iterative methods for linear systems, iterative methods for eigenvalue problems, fast Fourier transform, systems of nonlinear equations, and optimization.
Course material created by Professor Dietmar Saupe, Vladimir Bondarenko, and Ioan Cleju.
Target audience: Advanced undergraduate (3rd or 4th year)
Institution: Universität Konstanz
Materials available: Problem sets or projects, Lab materials, Course outline or syllabus, Downloadable code or data files
Products: MATLAB

Submitted: Jul 22, 2008



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