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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: [email protected] • • • • 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 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. 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. 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 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 Tentative Class Schedule Grading • • • • • Homework (6) Project (1) Exam (2) Quiz (2) Attendance 30% 10% 40% 10% 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. Classification of Handwritten Digits 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) Page Ranking for a Web Search Engine • Pagerank used in Google • HITS Face Recognition and Microarray Gene Expression Data analysis 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) 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?