intro

intro - CASCS565,DataMining Courselogistics Coursewebpage:

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CAS CS 565, Data Mining
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Course logistics Course webpage: www.cs.bu.edu/~evimaria/teaching.html Schedule: Mon – Wed, 4-5:30 Instructor: Evimaria Terzi,  evimaria@cs.bu.edu Office hours: Mon 2:30-4pm, Tues  10:30am-12 (or by appointment) Mailing list : cascs565a1-l@bu.edu
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Topics to be covered (tentative) Introduction to data mining and prototype  problems Frequent pattern mining  Frequent itemsets and association rules Clustering Dimensionality reduction Classification Link analysis ranking Recommendation systems Time-series data Privacy-preserving data mining
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Syllabus Sept 2 Introduction to data mining Sept 9 Basic algorithms and prototype  problems Sept 14, 16 Frequent itemsets and association  rules Sept 21, 23, 28, 30 Clustering algorithms Oct 5, 7 Dimensionality reduction Oct 12 Holiday Oct 14 Midterm exam Oct 19, 21, 26, 28 Classification Nov 2, 4, 9, 11 Link-analysis ranking Nov 16, 18, 23 Recommendation systems Dec 1, 3 Time series analysis Dec 8, 10 Privacy-preserving data mining
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Course workload Three programming assignments (30%) Three problem sets (20%) Midterm exam (20%) Final exam (30%) Late assignment policy : 10% per day up to  three days; credit will be not given after  that Incompletes will not be given
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Textbooks D. Hand, H. Mannila and P. Smyth: Principles of  Data Mining. MIT Press, 2001 Jiawer Han and Micheline Kamber: Data Mining:  Concepts and Techiques. Second Edition. Morgan  Kaufmann Publishers, March 2006 Toby Segaran: Programming Collective  Intelligence: Building Smart Web 2.0 Applications.  O’Reilly Research papers (pointers will be provided)
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Prerequisites Basic algorithms : sorting, set manipulation, hashing Analysis of algorithms : O-notation and its variants, perhaps  some recursion equations, NP-hardness Programming : some programming language, ability to do  small experiments reasonably quickly Probability : concepts of probability and conditional probability,  expectations, binomial and other simple distributions Some  linear algebra : e.g., eigenvector and eigenvalue  computations
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Above all The goal of the course is to learn and enjoy The basic principle is to ask questions when  you don’t understand Say when things are unclear; not everything  can be clear from the beginning Participate in the class as much as possible
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Introduction to data mining Why do we need data analysis? What is data mining?
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intro - CASCS565,DataMining Courselogistics Coursewebpage:

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