CS-6301 Data Representations, Fall 2018Friday 4:00 - 6:45PM, Room ECSS 2.412Instructor: Haim SchweitzerOffice: ECSS 3.602Office Hours:• Friday: 6:45-7:45PMTelephone: (972)883-2238 (for emergencies)Email: [email protected]: TBAOffice:Office Hours:•Telephone:Email:Course DescriptionData plays a fundamental role in modern scientific, engineering, and business applications. Many popularapproaches for data analysis and visualization depend on sophisticated representations. Examples include lowdimensional representations, clustering, classification, Euclidean embedding, and graph embedding. We willdiscuss classical and current state-of-the art algorithms for computing such representations from numericdata. These include various dimensionality reduction techniques, principal component analysis (PCA),singular value decomposition (SVD), clustering and spectral clustering, unsupervised and supervised featureselection, discriminant functions, etc. There are modern algorithms for computing these representations thatuse recently developed ideas such as randomization, and emphasize the algorithm performance on big data.The course will also cover the required mathematical background.TextsRequired TextMost of the material will be covered from class-notes with selected parts taken from sources available onthe web.There is no required text.Other material• A. Blum, J. Hopcroft, and R. Kannan: Foundations of Data Science.• T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. 2011. http://www-stat.stanford.edu/ tibs/ElemStatLearn/• R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification.Important Dates• Test 1: Friday, October 12, 2018.• Black Friday (no class), November 23, 2018.• Test 2: Friday, December 7, 2018.• Course Grades Available : December 20, 2018.Grading Policy• Ungraded Homework: 10%. (Assignments will not be graded.)• Graded Assignments (project(s)): 20%.• Test 1: 35%. (Open books.)• Test 2: 35%. (Open books.)Topics• Some linear algebra: Eigenvector Decomposition, Generalized Eigenvalue Problems, Singular Value De-composition• Multidimensional Scaling• Kernels• The Reservoir model of big data algorithms• Algorithms for random sampling• Unsupervised and Supervised Feature Extraction Techniques• Discriminant Functions• Clustering• Unsupervised and Supervised Feature Selection Techniques• Randomized Techniques for Data Representation and manipulation• Advanced TopicsPre-requisites• Pre-requisite: CS-5343Attendance1. Absence in three consecutive lectures will result in the course grade being lowered by one letter.2. Absence in four consecutive lectures will automatically result in a failing grade (F) in the course.SoftwareSome assignments will require software that can calculate eigenvectors and eigenvalues. A simple onlinecalculator is available at: http://www.bluebit.gr/matrix-calculator/ Other assignments and the projects willrequire the use of Python.Additional Policies• All exams are open books and notebooks.• Computers are not allowed in exams, but pocket calculators may be needed.• You must be present during the evaluation of your project.Student Conduct & DisciplineThe University of Texas System and The University of Texas at Dallas have rules and regulations for theorderly and efficient conduct of their business. It is the responsibility of each student and each student orga-nization to be knowledgeable about the rules and regulations which govern student conduct and activities.General information on student conduct and discipline is contained in the UTD publication, A to Z Guide,which is provided to all registered students each academic year.The University of Texas at Dallas administers student discipline within the procedures of recognizedand established due process. Procedures are defined and described in the Rules and Regulations, Board ofRegents, The University of Texas System, Part 1, Chapter VI, Section 3, and in Title V, Rules on StudentServices and Activities of the university’s Handbook of Operating Procedures. Copies of these rules andregulations are available to students in the Office of the Dean of Students, where staff members are availableto assist students in interpreting the rules and regulations (SU 1.602, 972/883-6391).A student at the university neither loses the rights nor escapes the responsibilities of citizenship. He orshe is expected to obey federal, state, and local laws as well as the Regents’ Rules, university regulations,and administrative rules. Students are subject to discipline for violating the standards of conduct whethersuch conduct takes place on or off campus, or whether civil or criminal penalties are also imposed for suchconduct.Academic IntegrityThe faculty expects from its students a high level of responsibility and academic honesty. Because the valueof an academic degree depends upon the absolute integrity of the work done by the student for that degree,it is imperative that a student demonstrate a high standard of individual honor in his or her scholastic work.Scholastic dishonesty includes, but is not limited to, statements, acts or omissions related to applicationsfor enrollment or the award of a degree, and/or the submission as one’s own work or material that is notone’s own. As a general rule, scholastic dishonesty involves one of the following acts: cheating, plagiarism,collusion and/or falsifying academic records. Students suspected of academic dishonesty are subject todisciplinary proceedings.Plagiarism, especially from the web, from portions of papers for other classes, and from any other sourceis unacceptable and will be dealt with under the university’s policy on plagiarism (see general catalog fordetails). This course will use the resources of turnitin.com, which searches the web for possible plagiarismand is over 90% effective.Email UseThe University of Texas at Dallas recognizes the value and efficiency of communication between faculty/staffand students through electronic mail. At the same time, email raises some issues concerning security andthe identity of each individual in an email exchange. The university encourages all official student emailcorrespondence be sent only to a student’s U.T. Dallas email address and that faculty and staff consideremail from students official only if it originates from a UTD student account. This allows the universityto maintain a high degree of confidence in the identity of all