기계 학습, Machine Learning 강좌 – 문일철 교수님
Week 01. Introduction——————————————
1. Motivation
2. MLE (Maximum likelihood Estimation)
3. MAP(Maximum a Posterior Estimation) – Bayes
4. Probability & Distribution
Supervised Learning
Week 02. Fundamentals of ML————————————-
1. Rule-Based ML
2. Decision Tree
– Entropy & Information Gain
noise & inconsistencies
3. linear regression (How to create a D.T)
http://archive.ics.uci.edu/ml/datasets/Housing
Week 03. Naive Bayes Classifier————————————-
1. Optimal Classification
pr( X|Y)가 Combination(Compound, joint)이 될때, 분포를 구하기 힘들다. –> Naive
2. Conditional Independence
Week 04. Logistic Regression ————————————-
1. Decision Boundary
Parameter Approximation of Logistic Regression
Gradient method
Week 05. SVM (Support vector machine) ————————————-
1. soft margin penalization
2. Kernel trick
Week 06. Training Testing and Regularization ————————————-
1. Over, Under fitting
2. Bias, Variance
3. Occam’s razor
4. Cross Validation
5 Performance Metrics
6 Regularization
Graphical Model====================================================
Week 07. Bayesian Network ————————————-
8 Potential Function and Clique Graph
Unsupervised Learning
Week 08. K-Means Clustering & Gaussian Mixture model ————————————-
Week 09. Hidden Markov Model ————————————-
– evaluation
– decoding
– learning
4 Viterbi Decoding Algorithm
5 Baum-Welch Algorithm
Week 10. Sampling Based Inference ————————————-
Sampling methods
– Forward Sampling
– Rejection Sampling
– Importance Sampling
Sampling Based inferense
– metropolis-Hastings
– Gibbs samling
