머신 러닝 II 강의 계획서
| 주차 | Topic | 내용 |
|---|---|---|
| 01 | Course Orientation, Intro to ML | Course Orientation, ML Introduction |
| 02 | PCA & SVD | Dimensionality Reduction, Principal Component Analysis, Singular Value Decomposition |
| 03 | Support Vector Machine 1 | Review (Linear/Logistic Regression), Geometry, Logistic Regression, Linear SVM, Concept of Margin |
| 04 | Support Vector Machine 2 | Slack Variables, Duality, Kernel Method, Code Exercise Download Codes: ◦ All sources (zipped) ◦ dataset only |
| 05 | Ensemble | Ensemble, Bagging, RandomForest, Code Exercise Download Codes: ◦ All sources (zipped) |
| 04 | Support Vector Machine 2 | Slack Variables, Duality, Kernel Method, Code Exercise Download Codes: ◦ All sources (zipped) ◦ dataset only |
| 05주차 | Ensemble, Bagging, RandomForest | Ensemble, Bagging, RandomForest, Code Exercise |
| 06 | Boosting, Stacking | AdaBoost, XGBoost, LightGBM, CatBoost, Code Exercise Download Codes: ◦ ensemble_randomforest.zip |
| 07주차 | Performance Metrics and Shap Values | Performance Metrics and Shap Values, Code Exercise Download Codes: ◦ shap_xgboost_practice.ipynb ◦ SHAP dataset guide (html) |
| 08주차 | Mid-term Exam |
(중간고사: 자료 없음) |
| 09주차 | Imbalanced Data, SMOTE | Imbalanced Data, Under Sampling, Over Sampling, SMOTE, BLSMOTE, DBLSMOTE, Code Exercise |
| 10주차 | Dev Env Setup, Neural Networks | Development Environment Setup, PyTorch Basics, MLP, DNN, ◦ Code Exercise: All sources (zipped) |
| 11주차 | TBA | TBD |
| 12주차 | TBA | TBD |
| 13주차 | TBA | TBD |
| 14주차 | TBA | TBD |
| 15주차 | Final Exam |
(기말고사: 자료 없음) |