FOML
1.Decision Tree
(1) Decision Tree / Source : SKKU IDC Lab Prof. Jaemin Cho
2.Chain Rule
Chain Rule, basics of Backpropagation / Source : SKKU IDC Lab Prof. Jaemin Cho
3.Ensemble Methods
Ensemble Methods - Bagging, RandomForest, Boosting, AdaBoost / Source - SKKU IDC Lab Prof. Jaemin Cho
4.Manifold and Feature Learning
Variance of Bagging, Derivation of a_t in AdaBoost, Manifold / Soruce : SKKU IDC Lab Prof. Jaemin Cho
5.Neural Net and Backpropagation
Neural Network, Backpropagation / Source : SKKU IDC Lab Prof. Jaemin Cho
6.Backpropagation
Backpropagation, Chain Rule / Source : SKKU IDC Lab Prof. Jaemin Cho
7.Clustering
K-means, Gaussian Mixture Model (GMM) / Source : SKKU IDC Lab Prof. Jaemin Cho
8.Clustering 2
Hierarchical Clustering, DBSCAN / Source : SKKU IDC Lab Prof. Jaemin Cho
9.Dimensionality Reduction
Dimensionality Reduction, PCA, t-SNE / Source : SKKU IDC Lab Prof. Jaemin Cho
10.Support Vector Machine (SVM)
SVM / Source : SKKU IDC Lab Prof. Jaemin Cho
11.SVM(2)
Non-linear SVM, Kernel Function, Kernel Trick / Source : SKKU IDC Lab Prof. Jaemin Cho
12.SVM(Final)
SVM Revisit / Source : SKKU IDC Lab Prof. Jaemin Cho