Download CDS 401 - George Mason University

George Mason University–Office of the Registrar Undergraduate Course Approval Form
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Department/Unit: _______CDS______________ Course Subject/Number: _____CDS 401_____________
Submitted by: ___John Wallin and Kirk Borne_____________ Ext: ___3-3617__ Email: [email protected]____
Course Title: _____Scientific Data Mining____________________________________________________
Effective Term (New/Modified Courses only): __Fall 2010 Final Term (deleted courses only):____________
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Prereq _X_ Coreq ___ (Check one):________CDS 302______________________________________
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COS Curriculum Committee approval: ______________________________________________ Date: ____________
Course Proposal Submitted to the COS Curriculum Committee
1. COURSE NUMBER AND TITLE:
CDS 401 – Scientific Data Mining
Course Prerequisites: CDS 302
Catalog Description:
2. COURSE JUSTIFICATION:
Course Objectives:
Students will be given a set of case studies and projects to develop and expand their understanding
of data mining and its scientific applications. This will provide a foundation for future data-centric
applications in their careers.
Course Necessity:
This course will be as an elective for majors in the Computational and Data Science majors as well as an
elective for students who are interested in learning more about this scientific data mining from other
disciplines.
Course Relationship to Existing Programs:
This course is uniquely tailored to the new program in Computational and Data Sciences. It may be used for
students in other majors who are interested in learning about this field. Courses in IT&E do cover some aspects
of this subject, but they are not tailored toward the unique aspects of data mining across the natural sciences.
Course Relationship to Existing Courses:
The content of this course is similar to some graduate courses within the CSI Ph.D. program. However, the
content will be suitably altered for an undergraduate audience.
3. APPROVAL HISTORY:
This course was approved by the CDS department as part of its proposed undergraduate degree program.
4. SCHEDULING AND PROPOSED INSTRUCTORS:
Semester of Initial Offering:
Spring 2010
Proposed Instructors:
Kirk Borne, Robert Weigel
5. TENTATIVE SYLLABUS: See attached.
CDS 401
SCIENTIFIC DATA MINING
-- SYLLABUS --
Prerequisites: CDS 302
Credits: 3
Instructor: Borne
Office Hours: TBD
Course Description:
This course provides a broad overview of the data mining component of the knowledge discovery
process, as applied to scientific research. Scientific databases are growing at near-exponential
rates. As the amount of data has grown, so has the difficulty in analyzing these large databases.
Data mining is the search for hidden, meaningful patterns in such databases. Identifying these
patterns and rules can provide significant competitive advantage to scientific research projects and in
other career settings. Data mining is motivated and analyzed as the “killer app” for large scientific
databases. Data mining techniques, algorithms, and applications are covered, as well as the key
concepts of machine learning, data types, data preparation, previewing, noise handling, feature
selection, normalization, data transformation, similarity measures, and distance metrics. Algorithms
and techniques will be analyzed specifically in terms of their application to solving particular
problems. Several scientific case studies will be presented from the science research literature. The
techniques that are presented will be drawn from well known statistical, machine learning,
visualization, and database algorithms, including clustering, decision trees, regression, Bayes
theorem, nearest neighbor, neural networks, and genetic algorithms. Topics will include informatics,
semantic knowledge mining, and the integration of data mining with large (and often distributed)
scientific databases.
Lecture Content:
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Data Mining Roots and Concepts
Scientific Motivation
Background Methods
o Statistics
o Machine Learning
o Visualization
o Rule-Based Algorithms
Software
o ADaM
o WEKA
o JOONE
o SNNS
o YALE
o Intelligent Miner
Data Preparation for Data Mining
o Data Types
o Feature Selection and Dimension Reduction
o Previewing
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o Cleaning
o Transformation and Normalization
o Distance Measures and Similarity Metrics
Supervised Learning Methods
o Decision Trees
o Artificial Neural Networks
o Bayes Networks
o Markov Models
Unsupervised Learning Methods
o Nearest Neighbor
o Clustering
o Link Analysis
o Association Mining
o Principal Components Analysis
o Outlier Detection
Kernel Methods
o Kernel-PCA
o Support Vector Machines
Science Case Studies
o Astronomy
o Physics
o Bioinformatics
o Drug Discovery
o Combinatorial Chemistry
o Remote Sensing, Earth Sciences, Geographic Information Systems
o Digital Libraries
o Autonomous Science Discovery Robots
Special Topics
o Text Mining
o Image Mining
o Temporal Mining
o Spatial Mining
High-Performance Data Mining
o Genetic Algorithms
o Distributed Data Mining
o Grid Mining
o Parallel Mining
Next Generation Mining
o Informatics
o Semantic Mining
o Knowledge Mining
Homework: (explain assignments)
Students will use on-line and computational resources learn about the paradigms, languages, and methods in
scientific datamining. Examples will be drawn from the a variety of scientific domains, and show students how
to use statistical and machine learning tools to approach this field.
Project: (describe, if applicable)
There will be no class project for this class.
Exams: (give details about midterm and final exams)
Midterm and final exams will be given, based on the content of the lectures and the homework assignments.
Short essays as well as analytic calculations about problem complexity, time, and simple examples from
programs will be used.
Grades:
Homework (40), Projects (%), Midterm (30%), Final Exam (30%)
Required Texts: (list)
M. H. Dunham, Data Mining: Introductory and Advanced Topics, 1 st Edition, Prentice-Hall, 2002.