This course will be offered remotely (遠端教學).
This course aims at equipping students with the theory foundation and the best
practice (as well as applications) of machine learning. The course contains four
major parts:
1. Theory foundation and algorithms of machine learning
2. Theory and practice of deep learning
3. Semester-long projects
4. Paper presentation
This course is designed to provide a graduate-level student a thorough
understanding in the methodologies, technologies, mathematics and algorithms
currently required by people who do research in machine learning. After attending
this course, students are expected to (a) have a solid grasp on machine learning
theories, (b) earn hands-on experience in using machine learning for real-world
problems, (c) be capable of catching up the latest machine learning literatures
and trends.
The lectures will be provided in Mandarin, and most of the teaching and research
materials will be in English. The materials and directions of this course are
tentative and subject to change.
1. Introduction
2. Statistical foundation
3. Convex optimization
4. Learning theory
5. Algorithms: decision tree
6. Algorithms: logistic regression, naive bayes classifier
7. Algorithms: support vector machine
8. Deep learning concept
9. Feedforward neural network (FFNN)
10. Basic components: activation function, loss function, etc
11. Techniques to fight overfitting: dropout and regularization
12. Minibatch and AdaGrad
13. Convolutional neural network (CNN)
14. Convolution filter, and max-pooling
15. CNN variants: res-net and others
16. Recurrent neural network (RNN) and Long-Short-Term Memory (LSTM)
17. embedding space
18. Deep Learning applications
Grading
R26; 5%: Paper Presentation
R26; 25%: Taking lecture notes with Latex
R26; 25%: Homework or pop quizzes
R26; 45%: Semester-long projects
R26; 無故不到2次成績不及格