Computer EngineeringCOEN281Patt Recog and Data Mining Spring2011
67863
Lec (67863)TTh 07:10-09pmKENNA 308How does an online retailer decide what product to recommend you based on your previous purchases? How do bio-scientists decide how many different types of a disease are out there? How computers rank web pages in response to a user query? In this two-part course we introduce some of the computational methods currently used to answer these and other similar questions. Some topics included are association rules, clustering, data visualization, logistic regression, neural networks, decision trees, ensemble methods and text mining. We'll describe covered algorithms as a "tuple" consisting of <task, model structure, score function, parameter search method, data management technique> which provides a useful framework for understanding and comparing different techniques.bulleted
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Introductory courses in probability and linear algebra (e.g., AMTH 210 and 245), and some programming experience beyond a first course (or permission from instructor).bulleted
Use the language R to conduct statistical and graphical analysis of data. Build regression and classification models from data.bulleted
There will be bi-weekly assignments in R to apply the methods discussed in class, and a take home final project. Bi-weekly work is to be done in groups of 2; partner will be assigned randomly for each project. bulleted
 bulletedDuda, Hart, Stork, "Pattern Classification", Wiley, 2nd ed., 2001.RequiredSeni, Elder, "Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions", Morgan and Claypool, 2010RequiredHand, Mannila, Smyth, "Data Mining", MIT Press, 2001.OptionalVenables, Ripley, "Modern Applied Statistics with S", Springer, 2003Optional
 bulletedWeek 1Introduction; RWeek 2Bayesian Decision Theory; Parameter Estimation2.1-2.6, 2.9; 3.1-3.4; see also 4.5 HMSWeek 3Linear Discriminant Functions; Regularization 3.8.2, 5.1-5.8; Ch.3 SEWeek 4Neural Networks6.1-6.6, 6.8, 8.3, 8.4Week 5Support Vector Machines5.11Week 6Decision Trees8.3; Ch.2 SEWeek 7Ensemble ModelsCh.4, Ch.5 SEWeek 8Clustering10.6, 10.7; see also 9.3-9.6 HMS 10.9, 10.10Week 9Non-metric: Association Rules; Visualization: SOMs 5.3.2 HMS; 10.14Week 10Text Retrieval14.1-14.3 HMS
Visit angel.scu.edu weekly for lecture notes. Post homework questions here first for the benefit of all students (instead of mailing the instructor directly).bulleted
Giovanni Senigraphic/giovanni.seni.gifRoleInstructorEmailgseni@scu.eduHoursBy AppointmentPersonal Pagehttp://gseni.minedata2learn.com/Stacie Riddlegraphic/371e245.gifRoleTAEmailcoen281@gmail.com