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