Download CS 636_adv_data_mining

CS 636 / CMPE 636 – Advanced Data Mining
Instructor’s Name:
Asim Karim
Year:
Office No. & Email:
429, [email protected]
Quarter: Winter
Office Hours:
TBA
Category: MS/PhD
TA for the Course:
TBA
Course Code
(Units)
Course
Description
2008-09
CS 636 / CMPE 636 – Adv. Data Mining
(3 Units)
This course will cover key developments in machine learning and data mining preparing students
for research work in these areas. A lecture-discussion format will be followed where topics are
introduced and techniques critically discussed. Each major topic will be introduced by the
instructor. Subsequently, students will lead discussion on selected papers in the topic area. All
students will be expected to read before coming to class and participate in the discussions.
Emphasis will be placed on the design and implementation of efficient and scalable algorithms for
data mining. The course project will require students to research, design, implement, and present
their solution to a machine learning or data mining problem.
Core/Elective
Elective. Strongly recommended for those who want to pursue research in data mining and
machine learning.
Pre-requisites
CS 536 Data Mining, or permission of instructor.
.
Goals
TextBooks,
Programming
Environment,
etc.
1.
2.
3.
Expose key research areas in data mining and machine learning
Develop article comprehension and critical review skills
Improve research and presentation quality for possible publication
Required Materials:
Core set of research articles and tutorials.
Reference Texts:
1. Pattern Recognition and Machine Learning, C. Bishop, Springer, 2006.
2. Learning with Kernels, B. Scholkopf and A.J. Smola, MIT Press, 2002.
3. Introduction to Information Retrieval, C.D. Manning, P. Raghavan, and H. Schutze, Cambridge
University Press, 2008 (e-book available freely on the Web).
CS 636 – Advanced Data Mining
Year:
2008-09
Quarter: Winter
Lectures,
Tutorials &
Attendance
Policy
Grading
Additional
Details
There will be 19 sessions (lectures-discussions) of 75 minutes each, and one in-class midterm
exam.
Attendance is essential, and attendance and class participation will be evaluated.
15%
40%
20%
10%
15%
Quizzes + Homework + summaries
Project (multiple sub-instruments and submissions)
Midterm Exam (8th week)
Attendance and class participation
Presentation/critique + Discussion
The course website will be the primary source for announcements and reading material including
lecture slides, handouts, and web links. http://chand.lums.edu.pk/~cs636w08
Cheating and plagiarism will not be tolerated and will be referred to the disciplinary committee for
appropriate action. Students may discuss with others; however, it is required that solutions are
written independently. Downloading code segments from the internet and presenting them as your
own work is considered plagiarism.
CS 636 – Advanced Data Mining
Year:
1.
2.
Topics
Kernel Methods and SVM: Dense and sparse kernel methods;
SVM for classification and clustering; large margin learning;
kernel selection and construction; applications to document
processing
Text Document Processing: Feature selection; information
extraction; semantic analysis; topic identification
2006-07
Quarter: Winter
Sessions
12
7