머신러닝 카테고리

1. Classifications

- SVM

- Bagged Decision Trees

- K-nearest neighbor

- Naïve Bayes

- Discriminant Models

- CNN

- RNN & LSTM

2. Regressions

- Linear Regression

- Nonlinear Regression

- Stepwise Regression

- Regularization

- Adaptive neuro-fuzzy

3. Clustering

- K-means clustering

- Hierarchical clustering

- Gaussian Mixture Model

- Hidden Markov Models

- Self-organizing Map

- Fuzzy C-means clustering

- Subtractive clustering

4. Probability

- Probability Theory

- Bayes Rule

- Total Probability Theorem

5. ETC

- Softmax(Classifier)

- ReLU(Rectified Linear Unit)

- Batch Normalization

- FC Layer

- Residual Network Concept

- Regularization vs. Lagrange Multiplier

- Maximum Likelihood Estimation

- Shannon Entropy 

- Cross Entropy 

- Maximum Entropy Models(Multinomial Logistic Regression)

- EM(Expectation-Maximization) Algorithm