Tentative topics are given below:
Topic 1: Variable selection under various models
Topic 2: Boosting methods for variable selection
Topic 3: Transfer learning for variable selection
Topic 4: Measurement error analysis
Note:
1. Students are expected to workload 3 hours in-class as well as outside-of-class.
2. 2023 Dec. 15 & 22 are (temporarily) scheduled as self-study for the preparation of the coming oral presentation and the final project.
Tentative schedule is given below:
Week 1: Introduction of this course, discuss the final project.
Weeks 2-4: Topic 1. Students are required to report research papers, discuss the final project, demonstrate relevant programming code.
Weeks 5-7: Topic 2. Students are required to report research papers, discuss the final project, demonstrate relevant programming code.
Weeks 8-10: Topic 3. Students are required to report research papers, discuss the final project, demonstrate relevant programming code.
Weeks 11-13: Topic 4. Students are required to report research papers, discuss the final project, demonstrate relevant programming code.
Weeks 14-15: Preparation of students' final project and the coming oral presentation as well as the relevant slides.
Week 16: Oral presentation of the final project. It will follow the standard invited session in the international conference to give a 30-min pressentation in English. After that, some comments and discussions will be given.
Week 17: Submit the final project in the paper format to the instructor.
Week 18: Discuss some feedbacks of the final project with the instructor.
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