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ENEE759G: Data Mining and Knowledge Discovery
Instructor: Joseph JaJa
Fall 2005 Course Syllabus
Course Objectives: The course will cover fundamental techniques used for analyzing
and classifying large scale scientific and business data. These techniques, primarily based
on machine learning and statistical methodologies, will include: statistical models and
patterns, supervised learning, Bayesian and neural networks, support vector machines,
search and optimization, finding patterns and rules, anomaly detection, and content based
retrieval.
Course prerequisites: Graduate standing
Prerequisite topics: Basic algorithms and optimization techniques, and a good
background in statistics. A background in nonlinear optimization is desirable.
Textbook: Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, and Vipin
Kumar, Pearson, Addison Wesley, 2006.
References:
1. Machine Learning, Tom Mitchell, McGraw-Hill, 1997.
2. Principles of Data Mining, D. Hand, H. Mannila, and P. Smyth, MIT Press, 2001.
3. An Introduction to Support Vector Machines, Nello Cristianini and John ShaweTaylor, Cambridge University Press, 2000.
4. Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. PiatetskyShapiro, P. Smyth, R. Uthurusamy, MIT Press, 1996.
Core Topics:
1. Data Preprocessing and Exploration (Chapters 2 and 3)
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Sampling
Principal Component Analysis and Singular Value Decomposition
Feature Extraction
Exploratory Data Analysis
2. Fundamental Classification Strategies (Chapters 4 and 5)
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Decision Trees
Bayesian Networks
Neural Networks
Support Vector Machines
3. Clustering Techniques (Chapter 8)
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Basic Clustering Techniques
Probabilistic Techniques (Maximum Likelihood and Mixture Modeling)
Cluster Evaluation
4. Mining for Rules (Chapter 6)
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Association Rules
Generalized Association Rules
Sequential Patterns
6. Anomaly Detection (Chapter 10)
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Statistical Approaches
Proximity-Based and Density Based Outlier Detection
Clustering-Based Techniques
Course Grade: Midterm (30%); Final (30%); Project (40%)
Project
Each student is expected to define a project that involves two of the general
techniques discussed in class and explain in some details their performances on at
least three significant data sets. A proposal explaining the project and the
experimental work to be carried out is due on September 29, 2005.
Information about software and data sets related to all the topics covered in class
can be found at the textbook web site: www-users.cs.umn.edu/~kumar/dmbook
Each student is supposed to make a presentation about her project during the last
two weeks of the class. Final reports are due December 8.
Contact Information: [email protected]; 301-405-1925.
Office: 3433 A.V. Williams Bldg; Office Hours: Monday, Wednesday 3-4:30
Midterm: Tuesday, October 18; Final: Wednesday, Dec. 21, 10:30-12:30 (may
be rescheduled)