Courses at University of Iowa and other places.

• 22C:031Algorithms  Fall 2015     Summer 2014  Spring 2014, Fall 2012, Spring2006, Spring 2007, Spring 2008, Fall2004
  Analysis of Algorithms Foundations, Sorting, searching,  Data Structures and Graph Algorithms. Prerequisite: 22C:017 (30), 22C:018 (20), 22C:019
  
  
• 22C:177 High Performance and Parallel Computing  Fall2014  Spring2012  Spring2008 Fall2005    
  Design and Implementation of Computational Linear Algebra Algorithms for High Performance Computers. The background of students is diverse. Basic knowledge of one computer language and basic linear algebra is required. Prerequisite: any math or computer science numerical or linear algebra course.
  
  
• 22C:019 Discrete Structures Fall 2012  Spring 2011 2005 (U. Iowa),   Spring 2007   Fall 2008,  Spring 2003    Structures needed to design and understand algorithms and data structures.  Logic, quantifiers (``for all ...'' and ``there is a ...'') and mathematical induction, sets, sequences, relations, functions and algorithms, counting methods and recurrence relations, graph theory applied to Computer Science.
  
  
• 22C:231  Design and Analysis of Algorithms Graduate Level (U. Iowa) 
  
• Analysis of Algorithms Foundations, Sorting, searching,  Data Structures and Graph Algorithms.
  
  
• 22C:021 Computer Science II: Data Structures   Spring2011
  
  
• 22C:196 Topics in Computer Science: Threads and GPUs  (Fall 2010) Parallel computing has become especially important lately with the widespread adoption of multicore processors and the use of Graphics Processing Units (GPUs) for mainstream computation.  Covered: OpenMP for shared memory and multicore computing, CUDA for GPU programming.
  
  
•  Pattern Recognition  (Fall 2009, Universidade de Sao Paulo, EESC, Brazil)
  Clustering, Classification, Discriminant Functions, Parameter Estimation, Graph Clustering, Statistics and Clustering,  Baysian Analysis, Least Squares, Maximum Likelihood.....
  
  
• Computacao Cientifica para Engenharia (Scientific Computing for Engineering) (Fall 2009, Universidade de Sao Paulo, EESC, Brazil ) & Computacao Cientifica Avancada(Advanced Scientific Computing) (Spring 2009, Universidade de Sao Paulo, ICMC, Brazil) IEEE arithmetic, Rounding, Exceptions, Cancellation. Stability, Efficiency in Time and Memory. Performance, Parallel Algorithms, MPI, OpenMP, current issues.

• 22C:196:002: Topics in Computer Science:   Advanced Scientific Programming (U. Iowa,Fall 2006),  Programnig with Threads and GPUs (U. Iowa,Fall 2010)

• 22C296 Topics in Computer Science: Numerical Computing (U. Iowa) ( Fall 2004): This course is about the design of numerical and scientific software. Topics include floating point arithmetic (round-off, underflow and overflow), algorithm stability, development of software, efficiency in time and memory, and tools for scientific programming.  Textbook: Numerical Computing with IEEE Floating Point Arithmetic, Michael L. Overton.  (Fall 2004). Parallel Algorithms (U. Iowa) (Fall 2003):We cover the design and implementation of diverse parallel algorithms. We implement programs for clusters using MPI. The application problems are from the broad area of scientific Computing. Textbook: Grama, Gupta, Karypis and Kumar, Introduction for Parallel Computing. 
  

• 22M072/22C:055 Introduction to Numerical Analysis (U. Iowa) (Spring 1998) Prerequisite:22M: 22 or 22M:36 or 22M:46. Textbooks: Kendall Atkinson, Elementary Numerical Analysis and Van Loan, Introduction to Scientific Computing.
  
 
• CPSC 659 - Parallel/Distributed Numerical Algorithms and Applications. ( Texas A&M) 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. Prerequisite: MATH 304, CPSC 653. Cross-listed with ELEN 659. Textbook: V. Kumar, A. Grama, A. Gupta and G. Karypis, Introduction to Parallel Computing.


• CPSC 653 - Computer Methods in Applied Sciences. (Texas A&M)
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, interpolation, numerical integration, linear algebraic systems, spline approximations, initial-value problems for ordinary differential equations, and finite-difference methods for partial differential equations. Prerequisite: CPSC 442 or MATH 417. Textbooks: D. Kincaid and W. Cheney, Numerical Analysis, and G. W Stewart, Afternotes on Numerical Analysis .
 
• CPSC210:021  Data structures. (Texas A&M)
Methods for organizing data; design of algorithms for efficient implementation and manipulation of data structures. Prerequisite: CPSC 120. Textbook: T. L. Naps, Introduction to Data Structures and Algorithm Analysis.
 
• CPSC 660 - Computational linear algebra.  (Texas A&M)
Techniques in matrix computation: elimination methods, matrix decomposition, generalized inverses, orthogonalization and least-squares, eigenvalue problems and singular value decomposition, iterative methods and error analysis. Prerequisite: MATH 417 or equivalent or CPSC 442 or equivalent. Cross-listed with MATH 660. Textbooks: N. N. Trefethen and D. Bau III, Numerical Linear Algebra. Golub and Van Loan, Matrix Computations.