Course Objectives: The course will cover core topics in machine learning and knowledge representation and inferencing. Topics covered will include: statistical learning models; decision trees; neural networks; Bayesian networks; support vector machines; clustering; propositional logic and inferencing; first-order logic and inferencing. Examples drawn from a wide range of applications will be introduced to motivate the main concepts introduced in the course.
Course prerequisites: Graduate standing
Prerequisite topics: Basic concepts in data structures and algorithms, Boolean logic, statistics, and object oriented programming.
Textbook: Introduction to Data Mining, Pang-Ning Tan, Michael Steinback, and Vipin Kumar, Pearson, Addison-Wesley, 2006.
References:
· Artificial Intelligence: A Modern Approach, S. Russell and P. Norvig, Prentice Hall, second edition, 2003.
Core Topics:
1. Introduction
Logic and Knowledge-Based Systems
Machine Learning Techniques
Basic Terminology and Related Applications
2. Machine Learning Framework
Data Representation and Basic Concepts
Statistics
Qunatization and Similarity Measures
Principal Component Analysis
3. Classification Concepts and Decision Trees
Introduction to Classification Concepts
Strategies for Building Decision Trees
The Overfitting Problem
Evaluation Strategies in Classification
4. Neural Networks
Basic Concepts and Methodology
Basic Learning Algorithm
Multi-layered Networks and the Back-Propagation Algorithm
5. Support Vector Machines
Maximum Marginal Classifiers
Brief Overview of Related Nonlinear Optimization Techniques
Nonlinear Support Vector Machines
6. Bayesian Networks
Maximum Likelihood and Maximum A Posteriori Estimates
Joint Probability, Bayes Theorem, and Conditional Independence
Naive Bayes Classifiers
Basic Concepts of Bayesian Networks and Related Inferencing
7. Clustering Techniques
Basic Concepts
The k-Means Algorithm
Hierarchical Clustering
Introduction to Probabilistic Techniques (Maximum Likelihood and Mixture Modeling)
8. Knowledge Representation and Inferencing
Introduction to Propositional Logic and inferencing
First-Order Logic and Inferencing
Knowledge Representation
Homework (15%) – almost weekly except for exam weeks
Two Midterms (25% each) – October 3, November 7
Final Exam (comprehensive, 35%) – December 15
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Instructor: |
Joseph JaJa |
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Office: |
3433 A. V. Williams |
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Office Hours: |
Tu, Th 3-5 or by appointment |
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Email: |
Joseph@umiacs.umd.edu |
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Phone: |
405-1925 |