Welcome to Advanced Topics of Machine Learning: Reinforcement and Online Learning
This course covered one or more selected topics of recent advances in machine learning. In particular, the course covered reinforcement learning and online learning.
After this course, you will be able to have the knowledge on
1. Underlying mathematical and algorithmic principles of reinforcement and online learning 2. The key factors that have made reinforcement and on-line learning successful for various applications
In particular, the course covers basic reinforcement (e.g., TD learning, Q learning and State Space Models), online learning (e.g., regret minimisation, stochastic vs. adversarial, full information, semi-bandit, and bandit feedback), and Monte Carlo Tree Search.
Machine Learning Basics
Organizer: Professor Bin Yang, Aalborg University, DK
Lecturers: Long Tran-Thanh, University of Warwick, UK
Time: (New dates and time) 2, 3, 9, and 10 November, morning from 10 to 12. The course will be online not physical.
Place: This course is a ONLINE course.
Number of seats: 60
Deadline: 19 October 2020
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 3,000 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 four 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: Bin Yang