Course homepage for "Business Analytics" @Korea University
-
비대면 기말고사 실시
- 일시: 2020년 12월 22일(화) 오후 3시 30분~5시 30분 (120분)
- 형식: Google Meet 회의 링크에 접속하여 시험시간 동안 비디오를 켠 상태에서 시험 실시
- 시험 방식: Open Slide + Cheating Sheet (A4용지 앞뒤로 3장, 총 6페이지, 본인이 직접 필기한 자료만 인정)
- 링크 및 시험 문제는 당일 오후 2시 25분에 블랙보드의 이메일 기능을 이용하여 전송
- 기말고사 제출 방식
- 시험이 종료되는 5시 30분 시점에서 답안지(A4 단면)를 페이지별로 휴대폰으로 촬영하여 담당교수 이메일(pilsung_kang@korea.ac.kr)로 전송 (촬영 및 발송시간 고려하여 이메일 발송시간 기준 5시 35분까지 인정)
- 12월 25일(금) 자정까지 Cheating Sheet와 답안지 원본을 스테이플러로 결합하여 담당교수 연구실로 제출(창의관 801A호, 부재중일 경우 문 아래로 밀어넣기)
-
유튜브 강의영상 요약 및 논문 재현 포스팅 기한 공지
- 유튜브 강의영상 요약 및 논문 재현 포스팅은 12월 27일(일) 23:59분까지 제출된 버전에 대해서만 인정하도록 하겠습니다.
- Syllabus (Document, Slide, Video)
- Tutorial resources (2015)
- Tutorial resources (2016)
- Dimensionality Reduction: Overview (Slide, Video)
- Supervised Methods 1: Forward selection, Backward elimination, Stepwise selection (Slide, Video)
- Supervised Methods 2: Genetic algorithm (Slide, Video)
- Unsupervised Method (Linear embedding) 1: Principal component analysis (PCA) (Slide, Video)
- Unsupervised Method (Linear embedding) 2: Multi-dimensional scaling (MDS) (Slide, Video)
- Unsupervised Method (Nonlinear embedding) 1: ISOMAP, LLE (Slide, Video)
- Unsupervised Method (Nonlinear embedding) 2: t-SNE (Slide, Video)
- Tutorial 1: Supervised Method
- Tutorial 2: Unsupervised Method (Linear embedding)
- Tutorial 3: Unsupervised Method (Nonlinear embedding)
- Theoretical foundation (Slide, Video)
- Support Vector Machine (SVM) - Linear & Hard Margin (Slide, Video)
- Support Vector Machine (SVM) - Soft Margin (Slide, Video)
- Support Vector Regression (SVR) (Slide, Video)
- Kernel Fisher Discriminant Analysis (KFDA) (Slide, Video)
- Kernel Principal Component Analysis (KPCA) (Slide, Video)
- 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)
- Anomaly Detection: Overview (Slide, Video)
- Density-based Anomaly Detection Part 1: Gaussian Density Estimation & Mixture of Gaussian Density Estimation (Slide, Video)
- Density-based Anomaly Detection Part 2: Parzen Window Density Estimation (Slide, Video)
- Density-based Anomaly Detection Part 3: Local Outlier Factor (LOF) (Slide, Video)
- Distance/Reconstruction-based Anomaly Detection (Slide, Video)
- Model-based Anomaly Detection Part 1: Auto-Encoder, 1-SVM, and Support Vector Data Description (SVDD) (Slide, Video)
- Model-based Anomaly Detection Part 2: Isolation Forest and Extended Isolation Forest (Slide, Video)
- (Optional) Anomaly Detection with Generative Adversarial Network (Video, presented by 김창엽)
- (Optional) Graph-based Anomaly Detection (Video, presented by 김혜연)
- Tutorial 8: Density-based novelty detection
- Tutorial 9: Distance/Reconstruction-based novelty detection
- Tutorial 10: Model-based novelty detection
- Overview (Slide, Video)
- Bias-Variance Decomposition (Slide, Video)
- Bagging (Slide, Video)
- Bagging: Random Forests (Slide, Video)
- Boosting 1 - Adaptive Boosting (AdaBoost) (Slide, Video)
- Boosting 2 - Gradient Boosting Machine (GBM) (Slide, Video)
- Boosting 3 - XGBoost (Slide, Video)
- Boosting 4 - Light GBM (Slide, Video)
- Boosting 5 - CatBoost (Slide, Video)
- (Optional) XGBoost (Video, presented by 윤훈상)
- (Optional) CatBoost (Video, presented by 김지나)
- Tutorial 11: Bagging
- Tutorial 12: AdaBoost, Gradient Boosting
- Tutorial 13: Random Forests, Decision Jungle (임희찬, 권상현)
- SSL: Overview (Slide, Video)
- SSL: Self-training & Co-Training (Multi-view algorithm) (Slide, Video)
- SSL: Graph-based SSL (Slide, Video)
- SSL: Generative Models (Slide, Video)
- (Optional) Text Augmentation (Video, presented by 김정희)
- (Optional) Semi-supervised learning with ladder network (Video, presented by 양우식)
- (Optional) MixMatch (Video, presented by 이정훈)
- (Optional) Remixaatch & FixMatch (Video, presented by 이정훈)
- Tutorial 14: Self-training
- Tutorial 15: Generative models
- Tutorial 16: Graph-based SSL
- Tutorial 17: Multi-view algorithm (Co-training)