Focus: One focus of the course is to learn how to read, summarize, present, review, and evaluate scientific papers. Moreover, you will get some exposure to current developments and research in artificial intelligence and related fields. The papers that will be discussed during the course originate from the following areas: AI and the Web, Machine Learning, Spatial Data Mining, Clustering, and general papers. There will be also some more general discussion on how to conduct a scientific research project. Finally, you will also learn how to write abstracts, introductions, conclusions, white papers, and executive summaries.
Reasons to take the course: The course is a good preparation for master thesis and PhD dissertation research in the areas machine learning, data mining, databases, data analysis, and artificial intelligence. Moreover, seeing how well-known scientists present their research results will hopefully help you to do a better job in conducting your own research and in presenting your research results in your future publications.
Course Elements: 2-3 quizes, paper walkthroughs, lectures introducing subfields of AI, on how to perform scientific reserach, and on how to present/read/evaluate papers, informal paper presentations by students, discussion of papers, reviewing of papers in a group, 2 general discussions, each student gives a single more formal presentation. There will be no programming and no course project in the course.
Grading: Quizes (45%), paper presentations (25%), paper review and other writing tasks (15%), Class Participation (15%) --- subject to change.
Only machine written solutions to homeworks and assignments are accepted (the only exception to this point are figures and complex formulas) in the assignments. Be aware of the fact that our only source of information is what you have turned in. If we are not capable to understand your solution, you will receive a low score. Moreover, students should not throw away returned assignments or tests.
Students may discuss course material and homeworks, but must take special
care to discern the difference between collaborating in order to increase
understanding of course materials and collaborating on the homework /
course project
itself. We encourage students to help each other understand course
material to clarify the meaning of homework problems or to discuss
problem-solving strategies, but it is not permissible for one
student to help or be helped by another student in working through
homework problems and in the course project. If, in discussing course materials and problems,
students believe that their like-mindedness from such discussions could be
construed as collaboration on their assignments, students must cite each
other, briefly explaining the extent of their collaboration. Any
assistance that is not given proper citation may be considered a violation
of the Honor Code, and might result in obtaining a grade of F
in the course, and in further prosecution.
last updated: March 3, 2007 at 7a
And finally: Frogland --- all about frogs