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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 =d51dd2&default_fun=&stu=¤t_lang=english 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