데이터사이언스 특강: 메타러닝
Meta Learning
Taesup kim
Goals
Deep Learning, and Meta Learning
Content
1. Introduction and deep learning
2. Multitask learning and transfer learning
3. Meta learning and few-shot learning
4. Optimization based meta learning
5. Model based meta learning
6. Meta reinforcement learning 1
7. Meta reinforcement learning 2
8. Project proposal
9. Online and continual learning
10.Modularization
11. Advanced meta learning 1
12. Advanced meta learning 2
13. Guest lecture
14. Project poster session
15. Project report submission and discussion
Textbook
Deep Learning (https://www.deeplearningbook.org), Dive into Deep Learning (https://d2l.ai)
Grading Policy
– Assignment: 30%
– Project: 50%
– Random Evaluation: 20%
Attendance Policy : Students who are absent for over 1/3 of the class will receive a grade of ‘F’ or ‘U’ for the course
(Exceptions can be made when the cause of absence is deemed unavoidable by the course instructor).
Prerequisite
Probabilistic Data Analysis, Machine Learning and Deep Learning for Data Science, Python programming