COSC 7363 --- Advanced Artificial Intelligence Spring 2007 ( Dr. Eick )


Basic Course Information

class meets: TU/TH 10-11:30a in 204 AH
Instructor: Dr. Christoph F. Eick
office hours (589 PGH): TU 11:30a-12:30p TH 4-5p
cancelled classes:
makeup classes:
class room: 204 AH

Course Information

Prerequisites: students should have taken one of the following courses: Artificial Intelligence (COSC 6368) or Machine Learning (COSC 6342) or Data Mining (COSC 6397) or a similar course. If you have doubts about prerequisites for COSC 7363 do not hesitate to contract Dr. Eick. Papers will be assigned to students based on their general background and interests.

Focus: One focus of the course is to learn how to read, summarize, present, 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. Moreover, brief, introductory 1-class lectures will be given for each subfield that is covered in the course. 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 presenting your research results in your future publications.

Course Elements: 2-3 quizes, paper walkthroughs, 5 introductory lectures to subfields covered and 2-3 lectures that center on how to present/read/evaluate papers, informal paper presentations by students, discussion of paper, reviewing of papers in a group, 2 general discussions. There will be no programming and no course project in the course.

Grading: Quizes (45%), paper presentations (25%), paper review (15%), Class Participation (15%) --- subject to change.

Course Tasks and Their Deadlines

January 18: Read Feigenbaum paper
January 25: Prepare Brin/Page walkthrough
February 1: Prepare Tong walkthrough
March 1: Quiz1
April 19: Quiz2

News COSC 6368 Spring 2006

Papers Covered

List of Papers Covered (evolving file)
Paper Download Directory

Schedule and Exam Dates 2007

Class Transparencies

Course Information COSC 6367
How to Read Scientific Papers

Grading

Translation number to letter grades:
A:100-90 A-:90-86 B+:86-82 B:82-77 B-:77-74 C+:74-70
C: 70-66 C-:66-62 D+:62-58 D:58-54 D-:54-50 F: 50-0

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: January 7, 2007 at 7a

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