CS570 - Machine Learning

Course Information
Course title:  CS570 - Topics in AI: Machine Learning 
Course description:  One of the many definitions of Machine Learning (ML) is "Any change in a system that allows it to perform better the second time on  repetition of the same task or on another task drawn from the same population" (Simon, 1983). Practically this means developing computer programs that automatically improve their performance through experience. The course covers the basic concepts and techniques of Machine Learning from both theoretical and practical perspective. The material includes classical ML approaches as Version Spaces, Decision Trees and Bayesian Learning as well as new approaches as Relational Learning and Minimum Description Length Principle (MDL). All topics are accompanied with hands-on exercises with implementations of ML algorithms. 
Course dates:  May 27, 2008 - Jul 17, 2008 
Location:  Online 
Course Goals
  • To introduce students to the basic concepts and techniques of Machine Learning.
  • To develop skills of using recent machine learning software for solving practical problems.
  • To gain experience of doing independent study and research.
Course Topics
  1. Introduction: machine learning problems, types of learning, designing a learning system
  2. Inductive learning: introducing basic concepts by example (learning semantic networks), general setting for induction
  3. Languages for learning: propositional (attribute-value), relational, Prolog
  4. Hypothesis space: version space learning
  5. Divide and conquer approaches: induction of decision trees, OneR, ID3
  6. Covering strategies: least general generalization approaches
  7. Searching the generalization/specialization graph
  8. Relational Learning and Inductive Logic Programming
  9. Evaluating hypotheses: error-based and MDL evaluation
  10. Bayesian learning
  11. Bayesian belief networks
  12. Instance-based learning
  13. Analytical (Explanation-Based) Learning
  14. Unsupervised learning: clustering
Required Textbook
Machine Learning, Tom Mitchell, McGraw , 1997, 0-07-042807-7 
Required Software
SWI-Prolog ( http://www.swi-prolog.org/ ) - free Prolog compiler, licensed under the GNU Public License. Portable to many platforms, including almost all Unix/Linux platforms, Windows, MacOS X and many more. More information about using SWI-Prolog for this course will be provided in the course content modules. 
Grading Policies
Grading will be based on five assignments and class participation through threaded discussions.

The letter grade will be determined by the following grading scale:
A A- B+ B B- C+ C C- D+ D D- F
950-1000 900-949 870-899 840-869 800-839 770-799 740-769 700-739 670-699 640-669 600-639 0-599

Late assignments will be marked one letter grade down for each 3 days they are late. 

It is expected that all students will conduct themselves in an honest manner and NEVER claim work which is not their own. Violating this policy will result in a substantial grade penalty or a final grade of F. 

General Web Resources
Instructor Information
Name:  Zdravko Markov, Ph.D
Email:  markovz@ccsu.edu
Phone:  (860) 832-2711 
Biography:  Dr. Zdravko Markov has an M.S. in Mathematics and Computer Science and a Ph.D. in Artificial Intelligence. He has been teaching and doing research in the area of Machine Learning for more than 15 years. Recently he developed a novel approach to conceptual clustering and is studying its application to Data Mining tasks. Dr. Markov has published 4 textbooks and more than 50 research papers in conference proceedings and journals. His most recent book (co-authored with Daniel Larose) is “Data Mining The Web: Uncovering Patterns in Web Content, Structure, and Usage", published by Wiley in 2007. Dr. Markov’s CCSU courses are in the areas of Computer Architecture and Design, Computing and Communication technology, Machine Learning, Data and Web Mining.