Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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”