Welcome to Advanced Topics in Machine Learning - Transfer Learning
Description: This course covers one or more selected topics of recent advances in machine learning. In particular, this year the course covers transfer learning.
A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data labeling efforts.
Prerequisites: Basic knowledge of machine learning.
(1) Review the recent progress and achievements on transfer learning.
(2) Understand the specific learning algorithms of transfer learning.
(3) Discuss the major future directions of transfer learning.
Organizer: Professor Bin Yang, email@example.com
Lecturers: Sinno Jialin Pan (Nanyang Technological University, Singapore)
Time: 25-26 June 2019
25 June, 10 to 16
26 June, 9 to 15
Place: Auditorium Novi 8, Alfred Nobelsvej 27
Zip code: 9220
Number of seats: 30
Deadline: 4 June 2019
Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
- Teacher: Chenjuan Guo
- Teacher: Bin Yang