## CMPE 482 - Numerical Linear Algebra and Its Applications## Spring 2017Instructor: Ali Taylan Cemgil ## Important announcement for taking CMPE482 in Spring 2017 for creditPrerequisite: Ability (or willingness to learn) programming in Octave and Python (numpy, scipy) and preparing reports on a jupyther notebook using latex and ipython. We won't teach you programming and programming projects are an important part. As CMPE482 is an elective course and there is no TA assigned this year, the class will be smaller this year. The projects will be turned in using git so you must be also familiar with it. CmpE students: Consent is required, I will accept only around 7-8 motivated students. Math undergraduates: In the past, many Math majors took the course but experience has shown that the lack of basic programming skills and the lack of computational thinking has been a major problem. This year, I will consider each consent request separately and I will be more selective. You need to have a high GPA (at least 2.75) and your transcript should reflect your programming background. I will approve your request with some delay (possibly by the last day of registration) and only if there are enough empty spots.
## AdministrativeAttendance and Participation in the lectures %10 Midterm %30 Final %30 3 Projects %30
## Final Project Bundle## Example Exams and projects## DescriptionIf you are interested in Machine learning, Data mining or Signal Processing, you shouldn't miss this course! Numerical linear algebra provides a set of basic methods that are useful for developing algorithms for a diverse spectrum of applications in data processing. At its heart, this field studies algorithms for performing linear algebra computations, most notably matrix operations. These elegant algorithms provide often fundamental solutions to engineering and computational problems, such as Image and signal processing, Information retrieval Data mining, Machine learning, Bioinformatics Optimization Computational Finance,
## TextbookTrefethen, Lloyd N. and Bau III, David; (1997). Numerical linear algebra. Philadelphia: Society for Industrial and Applied Mathematics. ISBN 978-0-89871-361-9
## Further ReferencesGolub, Gene H.; van Loan, Charles F. (1996), Matrix Computations, 3rd edition, Johns Hopkins University Press, ISBN 978-0-8018-5414-9
## Lecture SlidesLecture 01,02,03 - Matrix-Vector Multiplication, Orthogonal Vectors and Matrices, Norms.pdf Lecture 06,07,08,09 - Projectors, QR Factorization, Gram-Schmidt, Matlab.pdf Lecture 10,11 - Householder Triangularization, Least Squares Problems.pdf Lecture 14,18,19 - Stability, Cond of LS prob, Stab of LS alg.pdf Lecture 24,25 - Eigenvalue Problems, Eigenvalue Algorithms.pdf Lecture 26-27, To hessenberg form, RQ, Power, inserve iterations.pdf Lecture 30,31 - Other Eigenvalue Algorithms, Computing the SVD.pdf
Thanks to all that have participated in the -Lab- reading group series of Spring 2013!
Ismail Ari Hakan Guldas Umut Simsekli Onur Gungor Beyza Ermis Deniz Akyildiz Can Kavaklioglu Baris Fidaner Alp Kindiroglu Cem Subakan Baris Kurt
## SubmissionsWe will use github education https://github.com/CMPE482-Spring2017-Bogazici/course-description
## Computer UsageWe will use Octave and Python. See: Starting Octave
octave --force-gui Octave is a popular matlab clone. So, most (but not all) matlab material is useful ## Topics## I FundamentalsMatrix-Vector Multiplication Orthogonal Vectors and Matrices Vector and Matrix Norms Singular Value Decomposition **Application**: Document Retrieval, Latent Semantic indexing, Procrustes analysis
## II QR FactorizationProjectors Gram-Schmidt Orthogonalization, QR Factorization MATLAB Householder Triangularization Least Square Problems **Application**: Polynomial and Basis Regression
## III Conditioning and StabilityConditioning and Condition numbers Floating Point Arithmetic Stability
## V EigenvaluesEigenvalue Problems Overview of Eigenvalue Algorithms Reduction to Hessenberg or Tridiagonal form Rayleight Quotient, Inverse Iteration QR algorithm without/with shifts Computing the SVD **Application**: Spectral Clustering, Image segmentation
## Total Credits3 ## DedicationThis course is dedicated to the memory of our collegue and friend Ismail Ari (1983-2013). |