Download I Jen Chiang Course Information Course title DATA MINING

I Jen Chiang
Course Information
Course title
Semester
Department
DATA MINING
102-2
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
Ceiba Web Server
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/1022Biomed7083_
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
Course Description
7. Support vector machine
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
References
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”