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, 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.

Course Schedule

March 6+8; April 5+24+26: Student Presentations March 22: Quiz1
April 19: Quiz2
Homework1: February 29 Homework2: March 6 Homework3: March 29 Homework4: April 13

Organization COSC 7363 in Spring 2007

1. Goals and Objectives of COSC 7363
2. General Papers
- 2.1 What is AI?
- 2.2 Ed Feigenbaum Turing Award Lecture
- 2.3 Raj Reddy Turing Award Lecture
3. On Reading and Writing Papers
- 3.1 Elements of a Scientific Paper
- 3.2 How to Conduct a Scientific Research Project
- 3.3 Practical Exercises in Writing Abstracts, Introduction, and Conclusions
4. Artificial Intelligence for Information Retrieval
- 4.1 Page Rank
- 4.2 Deeper Inside Page Rank
- 4.3 Random Walk with Restart
- 4.4 Newer Developments with Respect to Search Engines
- 4.5 Content-based Image Retrieval
- 4.6 Using Relevance Feedback to Individualize Webpage
5. Clustering
- 5.1 Evaluating Cluster Quality
- 5.2 DBSCAN --- a Density-based Clustering Algorithm
- 5.3 Clustering with Bregman Divergences
6. Data Mining
- 6.1 What is Special about Spatial Data Mining?
- 6.2 Co-location Mining
- 6.3 Mining Spatio-Temporal Datasets
- 6.4 Statistics for Spatial and Spatio-Temporal Data Mining
- 6.5 Graph and Sequence Data Mining
7. How to Review Scientific Papers
- 7.1 How to be a Good Reviewer
- 7.2 Advice on Reviewing Papers
- 7.3 How to Respond to Reviewer Comments
- 7.4 Exercises in Reviewing Papers
8. Machine Learning
- 8.1 Reinforcement Learning
- 8.2 Meta Learning

Papers Covered

List of Papers Covered (evolving file)
Paper Download Directory

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: March 3, 2007 at 7a

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