Machine Learning - Spring
Instructor
Hung-Yi Lee
Schedule
Fri. 14:20 - 18:20
Open in Spring
Credits
4
Description
Please find all the policies and rules of this course below:
https://reurl.cc/r1O6Zb
https://reurl.cc/r1O6Zb
More introduction
1. Regression; Bias and Variance Errors
2. Classification; Logistic Regression
3. Dimensionality Reduction: Principle Component Analysis; Neighbor Embedding; Auto-Encoder
4. Semi-Supervised Learning
5. Neural Network Introduction: Gradient Decent; Back Propagation
6. Convolution/Recurrent Neural Network
7. Ensemble: Bagging and Boosting
8. Transfer Learning
9. Support Vector Machine; Convex optimization and Duality
10. Expectation Maximization, Gaussian Mixture Model, Variational Auto Encoder
11. Generalization Error: Rademacher complexity and VC dimension
3. Dimensionality Reduction: Principle Component Analysis; Neighbor Embedding; Auto-Encoder
4. Semi-Supervised Learning
5. Neural Network Introduction: Gradient Decent; Back Propagation
6. Convolution/Recurrent Neural Network
7. Ensemble: Bagging and Boosting
8. Transfer Learning
9. Support Vector Machine; Convex optimization and Duality
10. Expectation Maximization, Gaussian Mixture Model, Variational Auto Encoder
11. Generalization Error: Rademacher complexity and VC dimension
