The purpose of this course is to expose the student to several machine learning paradigms and provide in-depth understanding of selected methods. Homeworks will provide hands-on experience with selected learning methods and will test the student's understanding of topics discussed in class. All work in this course must be done individually.
The class project serves to further the student's understanding of one or more methods through an experimental or theoretical analysis of the learning methods. Students are required to obtain approval on a project proposal. Ideas for projects will be distributed in class.
Each student will make a class presentation on one or more technical papers in the area of machine learning to be approved by the instructor. The presentation must demonstrate an indepth understanding of the topic, including background material from the paper's references, and provide a critical review. Longer papers tend to be more self-contained; whereas, shorter papers will require more auxiliary material.
Lastly, the student is required to participate in class by submitting brief critiques of selected papers to be covered in class and participating in class discussions. The purpose of the critiques is to stimulate critical thinking and discussions on class topics. A critique is not merely a summary, but your own reactions to the content of the reading. Critiques should be approximately one page of single spaced type.
All assignments must be turned in on time. There will be no credit for late submissions. All work in this class must be done individually.
If you require an accommodation based on disability, please see me during the first week of the semester so we can be sure you are appropriately accommodated.
|5||9/11||Decision Tree Learning||Ch3||HW1|
|6||9/13||Decision Tree Learning|
|18||10/25||Learning Rule Sets||Ch10|
|19||10/30||Learning Rule Sets|
|22||11/8||Combined Learning||Project Proposal|
* Indicates that a written critique is due on that day covering papers to be distributed in class.