Machine Discovery (Fall 2016, 3 credits)
Instructor: Prof. Shou-de Lin (sdlin@csie.ntu.edu.tw)
Classroom: CSIE 104
Meeting Time: Mon 2:20-5:20 pm
Office Hour: After class or by appointment
TA : 楊鈞百,李廣和,許晉齊
Course Description:
It is widely accepted that machines can learn as good as human beings in many tasks: feeding a learning algorithm with sufficient amount of training data, it can generate a model that maps the inputs to the most plausible outputs. Discovery, on the other hand, is about finding something for the first time, which has never been observed in the training data. One interesting question to ask is whether machines can perform discovery when there is no labeled data available. This course will introduce techniques that allow machines to perform discovery. It will also cover several real findings in this topic. Such findings show that with the availability of large unlabeled data and powerful computation infrastructure, machines can indeed perform discovery tasks.
Grading:
Three Homework assignments (70%)
Final Project (30%)
Syllabus (Tentativee):
9月12日 | Intro to MD | |
9月19日 | PGM | HW1 Out |
9月26日 | PGM | |
10月3日 | PGM | |
10月10日 | No Class | |
10月17日 | PGM-based Discovery | |
10月24日 | Optimization | HW1 Due |
10月31日 | Optimization | |
11月7日 | Optimization | |
11月14日 | Optimization-based Discovery | |
11月21日 | No Class | HW2 Due, HW3 out |
11月28日 | Other Learning Models | |
12月5日 | Other Learning Models Based Discovery | |
12月12日 | Final Proposal | HW3 Due |
12月19日 | Knowledge Discovery and Pattern Mining | |
12月26日 | Knowledge Discovery and Pattern Mining | |
1月2日 | No Class | |
1月9日 | Final Presentation |