Data Mining
- Korea Univ. / Data Mining / STAT402 / 2022 Fall
- Data Mining using R & Python
- Book
- Lecture by Prof. Hyungjun Cho, Department of Statistics, Korea University
Lecture
| Chapter | Contents |
|---|---|
| 1 | Introduction to Data Mining |
| 2 | Data |
| 3 | Statistical Learning |
| 4 | Association Analysis |
| 5 | Predictive Modeling and Model Assessment |
| 6 | Regression Model |
| 7 | Neural Networks |
| 8 | Decision Tree |
| 9 | Ensembles |
| 10 | K-Nearest Neighbors(KNN) |
| 11 | Linear Discriminant Analysis(LDA) Quadratic Discriminant Analysis(QDA) |
| 12 | Support Vector Machine(SVM) |
| 13 | Clustering |
| 14 | Principal Component Analysis(PCA) |
| 15 | Project Presentation |
Assignment
| Num | Assignment |
|---|---|
| 1 | Association Analysis |
| 2 | Regression |
| 3 | Neural Networks & Decision Tree (+ Regressions) |
| 4 | Boosting, Bagging, Random Forests, KNN, LDA/QDA, SVM (+ Regression Models, Neural Networks, Decision Trees) |
| 5 | Hierachical Clustering & K-means Clustering |