Advanced machine learning

Lecture notes for courses Advanced machine learning (딥러닝 시대에도 필요한 고급기계학습).

Lecture Notes

• Graphical Model(GM)

Probability, Conditional Probability, Bayesian Reasoning, Independence

• Bayesian Network Overview

Probabilistic Reasoning, Bayesian Network, d-separation

• Inference in Bayesian Networks

Inference in Bayesian Networks, Inference in Polytrees

• Hidden Markov Model Overview

Markov Model, Hidden Markov Model

• Estimation of HMM

Baum-Welch Algorithm, Viterbi Algorithm

• Non-Linear SVM

Non-Linear SVM, Kernel Trick

• Random Forest

Bagging, Random Forest, Out-Of-Bag Error

• Gaussian Process

Gaussian Distribution, Covariance Matrix, Gaussian Process

• Singular Value Decomposition(SVD)

Linear Transformation, eigenvalue, eigenvector, Singular Value Decomposition

• Matrix Factorization

Matrix Factorization Variations

• Gaussian Mixture Model(GMM)

Gaussian Mixture Model

• Expectation-Maximization(EM) Algorithm

Expectation-Maximization Algorithm, Jensen's Inequality, GMM by EM Algorithm

:mag: Syllabus

1. Graphical Model

2. Bayesian Network

3. Hidden Markov Model

4. Genetic Algorithm

5. Support Vector Machine

6. Random Forest

7. Adaboost

8. Gaussian Process

9. Matrix Factorization

10. Gaussian Mixture Model