資料採礦
Data Mining
| 節 | 週三 |
|---|---|
5 13:20–14:10 | 資料採礦 MB415（光復） 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
Learning how to apply data-science skills to manufacturing and service sectors and following a PDCA loop (plan-do-check-action)
Statistics (required), Programming (C/Python/R), Database (plus)
R Python WEKA R Python WEKA
R-language programming Open-source dataset
1. Take-home assignments (4 times) 40% 2. Academic paper & project presentation 20% 3. Concept testing 40% (midterm & final exam)
Statistical computation & unsupervised learning
1. Statistical analysis 2. Hypothesis testing 3. Unsupervised clustering 4. Association rule mining
- 講授：
- X
- 示範：
- X
- 實作：
- X
備註：R Python WEKA
Machine learning & ensemble learning
1. Classifier design 2. Regressor design 3. Neural network 4. Support vector machine
- 講授：
- X
- 示範：
- X
- 實作：
- X
備註：R Python WEKA
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to data science 2026-09-09(三) |
| 第 2 週 | Introduction to R programming 2026-09-16(三) |
| 第 3 週 | Data preprocessing 2026-09-23(三) |
| 第 4 週 | Statistical analysis 2026-09-30(三) |
| 第 5 週 | Hypothesis testing (Chi-square/Proportion test/T-test/ANOVA) 2026-10-07(三) |
| 第 6 週 | Unsupervised clustering (K-means, C-means, Gaussian mixture, hierarchical clustering, DBSCAN) 2026-10-14(三) |
| 第 7 週 | Association rule mining (apriori) 2026-10-21(三) |
| 第 8 週 | Basic classifiers (KNN, Naive Bayes, Logit regression) 2026-10-28(三) |
| 第 9 週 | Midterm research-proposal presentation 2026-11-04(三) |
| 第 10 週 | Midterm exam concept test 2026-11-11(三) |
| 第 11 週 | Decision tree (CART, C4.5, C5.0) 2026-11-18(三) |
| 第 12 週 | Ensemble learning (random forest, gradient boosting, adaboost) 2026-11-25(三) |
| 第 13 週 | Ensemble learning & amp amp& #x0D amp ROC (receiver operating characteristics) 2026-12-02(三) |
| 第 14 週 | Advanced & amp amp& #x0D amp biased regression (MARS, Ridge, Lasso) 2026-12-09(三) |
| 第 15 週 | Support vector machine (SVM) 2026-12-16(三) |
| 第 16 週 | Artificial neural network (ANN) 2026-12-23(三) |
| 第 17 週 | Final exam concept test 2026-12-30(三) |
| 第 18 週 | Final project presentation 2027-01-06(三) |
Introduction to data mining, Pang-Ning Tan Data mining (concepts and techniques), Han ＆amp; Kamber, Morgan Kaufman. Machine learning, Mitchell, McGraw-Hill. Applied data mining, Paolo Giudici, Wiley (全華代理) Data mining for business intelligence, Galit Shmueli et al., Wiley Data mining, Roiger ＆amp; Geatz, Addison-Wesley (東華代理) Next generation of data mining applications, Kantardzic ＆amp; Zurada, IEEE society. Pattern classification, Duda et al., Wiley-interscience. Educational training course materials (產學訓練自編教材)
- 地點
- R411
- 時間
- Wed. EF
- 聯絡方式
- chihwang@mail.nctu.edu.tw