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Course Information
Course title
Semester
Department
DATA MINING
101-2
COLLEGE OF ENGINEERING GRADUATE INSTITUTE OF
BIOMEDICAL ENGINEERING
Instructor
I Jen Chiang
Administrative Curriculum
Number
548EM1240
Teaching Curriculum Number
Biomed7083
Class
Credits
Full/Half Yr.
Required/Elective
Time
Remarks
BASIC MEDICAL BLDG.ROOM NO.507(基 醫 507)
3
Half
Elective
Wednesday 234
The upper limit of the number of students: 20
https://ceiba.ntu.edu.tw/modules/index.php?csn
Ceiba Web Server
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Table of Core Capabilities and
Curriculum Planning
Course Syllabus
1. Concept of data mining
2. Knowledge representations and deduction inferences
3. Uncertainties and reasoning in medicine
4. Graph models and Bayesian networks
5. Statistics and hypothesis testing
6. Regression analysis
7. Support vector machine
Course Description
8. Classifications
9. Decision trees and lazy learning
10. Association rules
11. Clustering
12. Feature selections
13. Artificial neural networks and genetic algorithms
14. Rough sets
15. Combining inductive and analytical learning
16. Boosting, stacking, and bagging
17. Computational learning theory
18. Temporal sequent analysis
19. Text mining
Course Objective
The key objectives of this course are two-fold: (1) to teach the
fundamental concepts of data mining and techniques and (2) to
provide extensive hands-on experience in applying the concepts
to real-world applications from UCI machine learning
repositories by using statistic R. The core topics to be covered
in this course include classification, clustering, association
analysis, temporal/sequence analysis, and cloud data analysis.
Course Requirement
Office Hours
Tuesdays: 4:30-7:30 pm in BME biomedical informatics lab.
Hastie, Tibshirani, Friedman, “The Elements of Statistical Learning”
J. Han and M. Kamber, “Data Mining: Concepts and Techniques”, 3rd.
T. Mitchell, “Machine Learning”
References
Designated reading
No.
Item
1. Homework
2. Quiz
%
Explanations for the conditions
20 %
%
Grading
3. Midterm #1
30 %
4. Midterm #2
%
5. Final report
50 %
Progress
Week
Date
Topic
Week 1
Introduction
Week 2
Overview of Data Mining
Week 3
Statistical Issues and Power Analyses
Week 4
Clustering I (Distance-based, Model-based)
Week 5
Regressions and Predictions
Week 6
Association Rules and Correlation Rules
Week 7
Artificial Neural Networks (Proceptrons, MLPs)
Week 8
Artificial Neural Networks (RBFs, ARTs, SOMs, …)
Week 9
Midterm Exam.
Week 10
Bayes Theory and Bayesian Networks
Week 11
Tree-based classifications (C4.5, CART, …)
Week 12
Attribute Selections and Feature Selections (including the concept of Rough Sets)
Week 13
Time Serial and Sequence Analyses
Week 14
Support Vector Machines
Week 15
Clustering II
Week 16
Students’ Final Presentations
Week 17
Students’ Final Presentations