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CSE 494/598: Numerical Linear Algebra for Data Exploration

CSE 494/598: Numerical Linear Algebra for Data Exploration. Jieping Ye Department of Computer Science and Engineering Arizona State University http://www.public.asu.edu/~jye02. Course Information. Instructor: Dr. Jieping Ye Office: BY 568 Phone: 480-727-7451 Email: jieping.ye@asu.edu

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CSE 494/598: Numerical Linear Algebra for Data Exploration

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  1. CSE 494/598: Numerical Linear Algebra for Data Exploration Jieping Ye Department of Computer Science and Engineering Arizona State University http://www.public.asu.edu/~jye02

  2. Course Information • Instructor: Dr. Jieping Ye • Office: BY 568 • Phone: 480-727-7451 • Email:jieping.ye@asu.edu • Web: www.public.asu.edu/~jye02/CLASSES/Fall-2007/ • Time: MW 10:40AM - 11:55AM • Location: BYAC 110 • Office hours: MW 2:30pm--4:00pm

  3. Course Information (Cont’d) • Prerequisite:Basic linear algebra skills. • Course textbook:Matrix Methods in Data Mining and Pattern Recognition. by Lars Elden, 2007. • Objectives: • teach the basics of numerical linear algebra • provide extensive hands-on experience in applying the linear algebra techniques to real-world applications.

  4. Course Information (Cont’d) • The Matrix Cookbook, by Kaare B. Petersen and Michael S. Pedersen. Available on-line at http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=3274 • Introduction to Linear Algebra, by Gilbert Strang, 2003. • Applied Numerical Linear Algebra, by James W. Demmel, 1997. • Matrix Computations, by Gene H. Golub and Charles F. van Loan, 1996. • Pattern Recognition and Machine Learning, by Christopher M. Bishop, 2006. • The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by T. Hastie, R. Tibshirani, and J. Friedman, 2001.

  5. Topics: Part I • Linear algebra background • Vectors and Matrices • Linear Systems and Least Squares • Singular Value Decomposition • Reduced Rank Least Squares Models • Tensor Decomposition • Clustering and Non-Negative Matrix Factorization

  6. Topics: Part II • Applications • Classification of Handwritten Digits and face images • Text Mining • Page Ranking for a Web Search Engine • Automatic Key Word and Key Sentence Extraction • Massive data compression using tensor SVD • Clustering and classification of Microarray gene expression data • Gene expression pattern image classification and retrieval

  7. Tentative Class Schedule

  8. Grading • Homework (6) 30% • Project (1) 10% • Exam (2) 40% • Quiz (2) 10% • Attendance 10% • Assignments and projects are due at the beginning of the lecture. Late assignments and projects will not be accepted. Attendance to lecture is mandatory.

  9. Classification of Handwritten Digits

  10. Text Mining • Understand methods for extracting useful information from large and often unstructured collections of texts. • Another closely related term is information retrieval. • Vector space model for document representation • Create a term-document matrix • Each document is represented by a column vector • Latent Semantic Indexing (LSI)

  11. Page Ranking for a Web Search Engine • Pagerank used in Google • HITS

  12. Face Recognition and Microarray Gene Expression Data analysis

  13. Gene Expression Pattern Image Analysis (a-e) Series of five embryos stained with a probe (bgm) (f-j) Series of five embryos stained with a probe (CG4829)

  14. Survey • Why are you taking this course? • What would you like to gain from this course? • What topics are you most interested in learning about from this course? • Any other suggestions?

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