Course homepage for "Business Analytics" @Korea University
- Final Exam:
- Time: 2018-12-18 14:00~17:00
- Place: New Engineering Hall 218, 224 (download)
- Final exam & Tutorial Note score sheet
- Course syllabus: download
- Tutorial resources (2015)
- Tutorial resources (2016)
- Dimensionality Reduction: Overview
- Supervised Methods: Forward selection, Backward elimination, Stepwise selection, Genetic algorithm
- Unsupervised Method (Linear embedding): Principal component analysis (PCA), Multi-dimensional scaling (MDS)
- Unsupervised Method (Nonlinear embedding): ISOMAP, LLE, t-SNE
- Tutorial 1: Supervised Method
- Tutorial 2: Unsupervised Method (Linear embedding)
- Tutorial 3: Unsupervised Method (Nonlinear embedding)
- Theoretical foundation
- Support Vector Machine (SVM)
- Support Vector Regression (SVR)
- Kernel Fisher Discriminant Analysis (KFDA)
- Kernel Principal Component Analysis (KPCA)
- Tutorial 4: Support Vector Machine (SVM)
- Tutorial 5: Support Vector Regression (SVR)
- Tutorial 6: Kernel Fisher Discriminant Analysis (KFDA)
- Tutorial 7: Kernel Principal Component Analysis (KPCA)
- Novelty detection: Overview
- Density-based novelty detection
- Distance/Reconstruction-based novelty detection
- Model-based novelty detection
- Applications
- Tutorial 8: Density-based novelty detection
- Tutorial 9: Distance/Reconstruction-based novelty detection
- Tutorial 10: Model-based novelty detection
- Motivation and theoretical backgrounds
- Bagging
- Boosting: AdaBoost, Gradient Boosting
- Tree-based Ensemble: Random Forests, Decision Jungle
- Tutorial 11: Bagging
- Tutorial 12: AdaBoost, Gradient Boosting
- Tutorial 13: Random Forests, Decision Jungle (임희찬, 권상현)
- Overview
- Self-training
- Generative models
- Semi-supervised SVM
- Graph-based SSL
- Multi-view algorithm (Co-training)
- Tutorial 14: Self-training
- Tutorial 15: Generative models
- Tutorial 16: Graph-based SSL
- Tutorial 17: Multi-view algorithm (Co-training)