고급 인공지능
- 1. Introduction [PDF]
- 2. Supervised Learning [PDF]
- 3. Bayesian Decision theory [PDF]
- 4-1. Parametric Methods - 1 [PDF]
- 4-2. Parametric Methods - 2 [PDF]
- 5. Multivariate Methods [PDF]
- 6-1. Dimensionality Reduction - 1 [PDF]
- 6-2. Dimensionality Reduction - 2 [PDF]
- 6-3. Dimensionality Reduction - 3 [PDF] (wiki: Nonlinear dimensionality reduction) (t-SNE demo)
- 7. Clustering [PDF]
- 8. Nonparametric Methods [PDF]
- 9. Decision Trees [PDF]
- 10. Linear Discrimination [PDF]
- 11. Multilayer Perceptrons [PDF]
- 11-2. Convolutional Neural Network (taken from http://www.cse.ust.hk/~leichen/courses/FYTG-5101) [PDF]
- 11-5. RNN [PDF]
- 11-6.Gradient descent, momentum method, adaptive learning rate, and rmsprop (taken from http://www.cs.toronto.edu/~tijmen/csc321/) [PDF-2] (SGD, momentum, and RMSprop - 1) (SGD, momentum, and RMSprop - 2)
