- Introduction to Machine Learning (ML)
1-1. Intro to ML (1)
1-2. Intro to ML (2); What will we learn in this course - Probability and Bayesian Learning – ML 공부를 위한 간단한 확률 개념
2-1. Random variable, Conditional probability, and Bayes Rule
2-2. Posterior, Likelihood and Prior
2-3. MAP and MLE; Number game case study - Linear Regression
3-1. Linear regression basic
3-2. Linear regression w/ Probability density
3-3. Python practice for linear regression - Logistic Regression
4-1. Logistic regression basic & python practice
4-2. Application for higher dimension and multiple class - k-NN Classification & Python practice
- Clustering
6-1. k-Means & EM algorithm
6-2. Gaussian mixture model (1)
6-3. Gaussian mixture model (2)
6-4. Python practice for k-Means
6-4. Python practice for GMM Clustering - Support vector machine (SVM)
7-1. Intro to SVM
7-2. Intro to Kernel trick
7-3. Lagrange Multiplier and KKT condition for SVM
7-4. SVM development (1) -본격 SVM 배우기
7-5. SVM development (2) -본격 SVM 배우기
7-6. Soft margin and kernel trick for SVM
7-7. Python practice for SVM - Artificial Neural Network; Deep learning basic
8-1. Perceptron
8-2. Multi-layer perceptron (MLP)
8-3. Training for MLP
8-4. Python practice for MLP - Convolution Neural Network (CNN)
9-1. Basic for CNN
9-2. CNN case; Le-Net
9-3. Various CNN structures
2 replies on “머신러닝 (Machine Learning) 2021 Spring Version”
좋은 강의 감사합니다.
감사드립니다