Download Course Plan/Proposal

Course Plan/Proposal
Instructor's Name：YEN SHOW-JANE
Year：97
Semester：2 (1-autumn term 2-spring term)
Class Number：36555
Course Number：45131
Course Name：Machine Learning and Data Mining
Total credits：3
Weekly classroom hours：3
Department：36 Computer Sciences and Information Engineering
Is this course a
Course
Objective
Course
Outline
Course
Outcomes
Department
Education
Goal
semester Course ?
elective
Course ?
Upon completion of this course, students will understand:
1.The concepts and applications of machine learning and data mining.
2.Data mining on customer relationship management.
3.Various types of algorithms for mining association rules.
4.Various types of algorithms for mining sequential patterns.
1.The Concepts and Applications of Machine Learning and Data Mining
2.Association Rule Mining Algorithms: Apriori, DHP, FPGrowth, H-mine
3.Multidimensional Association Rule, Multiple-level Association Rule, Weighted
Association Rule, Quantitative
Association Rule, Constraint Association Rule Mining Algorithms
4.Sequential Pattern Mining Algorithms: AprioriAll, Prefixspan
5.Multidimensional Sequential Pattern, Multiple-level Sequential Pattern,
Weighted Sequential Pattern,
Quantitative Sequential Pattern, Time-Gap Sequential Pattern, Constraint
Sequential Pattern Mining Algorithms
1.Describe the concepts and applications of machine learning and data mining.
2.Apply data mining algorithms for mining binary association rules,
multidimensional association rules,
multiple-level association rules, weighted association rules, quantitative
association rules and association rules
with constraints from transaction databases, and compare the differences among
these mining association rule
algorithms.
3.Apply data mining algorithms for mining binary sequential patterns,
multidimensional sequential patterns, multiplelevel sequential patterns, weighted sequential patterns, quantitative
sequential patterns, time-gap sequential
patterns and sequential patterns with constraints from transaction databases,
and compare the differences among
these sequential pattern mining algorithms.
◎Imparting Advanced Knowledge
Strengthen fundamental theoretical and technical aspects to develop
professional skills of computer science and information engineering.
◎Building Up Academic Research Competence
Foster capability in problem discernment and problem solving, and ability to
composite academic articles to facilitate participation of academic
conferences.
◎Promoting Effective Communicate Skills and Team Work Spirit
Emphasize oral and written proficiency in conveying research progress and
outcomes, and prepare the readiness to work collaboratively.
◎Enhancing All-around Views
Ensure the awareness of rapidly developing disciplines, and ability to
comprehend contemporary issues.
Department
Core
Competency
◎Prepare students with practical professional knowledge (Solid
Foundation)
◎Prepare students with competence in independent thinking and research (Problem
Solving)
◎Prepare students with capability to study, write, and orally report
professional articles (Effective Communication)
Prerequisite None
Course
Text &
Course Slides & Papers!
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Text &
Reference
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Course Slides & Papers!
Ordinary：100%。
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