Advanced machine learning

K-MOOC • 2023
Instructor : Jee-Hyong Lee(Professor, Sungkyunkwan University)

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

🔍 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