CSCI/ARTI 8950 Machine Learning

Spring 2008: Tuesdays and Thursdays 3:30pm - 4:45pm & Wednesdays 3:35pm - 4:25pm, Boyd GSRC 208

Instructor: Prof. Khaled Rasheed
Telephone: (706)542-3444
Office Hours: Tuesday: 1-2:30pm and Wednesday: 4:35-6:00pm or by email appointment
Office Location: Room 219B, Boyd GSRC


Machine learning is a sub-field of artificial intelligence which is concerned with computer programs that can automatically improve their capabilities and/or performance by acquiring (learning) experience. The main objectives of this course are to provide students with an in-depth introduction to machine learning theory and methods and an exploration of research problems in machine learning and its applications which may lead to work on a project or a dissertation. The course is intended primarily for computer science and artificial intelligence graduate students. Graduate students from other departments who have a strong interest and sufficient experience in artificial intelligence may also find the course interesting.

Recommended Background:

CSCI/PHIL 4550/6550 Artificial Intelligence or CSCI 4560/6560 Evolutionary Computation (or permission of the instructor). Familiarity with basic computer algorithms and data structures and at least one high level programming language.

Topics to be Covered:

  • Part I: Machine learning techniques: Selected from inductive learning, decision trees, neural network approaches, evolutionary computation approaches and classifier systems, reinforcement learning, statistical and Bayesian learning, instance-based learning, explanation-based learning and computational learning theory.
  • Part II: Machine learning applications: Selected from data mining, biomedical modelling, medical diagnosis, text classification, pattern recognition and/or other contemporary applications.

    Expected Work:

    Reading; assignments (some include programming and/or running existing programs); midterm; final and term project and paper. (Unless otherwise announced by the instructor: all assignments and all exams must be done entirely on your own.)

    Academic Honesty and Integrity:

    All academic work must meet the standards contained in "A Culture of Honesty." Students are responsible for informing themselves about those standards before performing any academic work. The penalties for academic dishonesty are severe and ignorance is not an acceptable defense.

    Grading Policy:

  • Assignments: 30% (Programs, homeworks, attendance, paper presentation)
  • Midterm Examination: 20%
  • Final Examination: 20%
  • Term Project: 30% (includes term paper and presentation)
    Students may work on their term projects in groups of up to three students each. The above distribution is only tentative and may change later. The instructor will announce any changes.

    Assignment Submission Policy

    Assignments must be turned in by the assigned deadline. Late assignments will not be accepted. Rare exceptions may be made by the instructor only under extenuating circumstances and in accordance with the university policies.

    Course Home-page

    A variety of materials will be made available on the ML Class Home-page at, including handouts, lecture notes and assignments. Announcements may be posted between class meetings. You are responsible for being aware of whatever information is posted there.

    Lecture Notes

    Copies of some of Dr. Rasheed's lecture notes will be available at the bottom of the class home page. Not all the lectures will have electronic notes though and the students should be prepared to take notes inside the lecture at any time.

    Textbook in Bookstore

  • "Machine Learning", Tom Mitchell. McGraw-Hill, 1997. (Required.)

    Additional Books

  • "Data Mining: Practical Machine Learning Tools and Techniques (2nd edition)", Ian Witten & Eibe Frank. Morgan Kaufmann, 2005.
  • "Evolutionary Computation : Towards a New Philosophy of Machine Intelligence", David Fogel. IEEE press, 1999.

    Web Resources

  • David Aha's Machine Learning Resources
  • University of California at Irvine ML Repository
  • The WEKA Machine Learning Project


  • "Mining Distance Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule" Bay and Schwabacher, 2003. [David Luper][4-2]{download}
  • "Link Mining Applications: Progress and Challenges" Ted Senator, 2005. [Yong Wu][4-2]{download}
  • "Facial expression recognition from video sequences: temporal and static modeling" Cohen et al., 2003. [Devangana Kar][4-3] {download}
  • "An introduction to variable and feature selection" Guyon and Ellisseeff, 2003. [Yingfeng Wang][4-8]{download}
  • "Online Feature Selection for Pixel Classication" Glocer et al., 2005. [Anousha Mesbah][4-9] {download}
  • "Fusion of LDA and PCA for Face Recognition" Marcialis and Roli, 2005. [Zhibin Huang][4-9] {download}
  • "Modern Information Retrieval: A Brief Overview" Amit Singhal, 2001. [Dong Zhang][4-9]{download}
  • "A Machine Learning Approach to Keystroke Dynamics Based User Authentication" Revett et al., 2007. [Kushel Bellipady][4-10]{download}
  • "Face Recognition using Eigenfaces and Neural Networks" Rizon et al., 2006. [Shiva Sandeep][4-15]{download}
  • "Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models" Rasheed and Hirsh, 2000. [Meng Meng][4-15]{download}
  • "Learning to Detect and Classify Malicious Executables in the Wild" Kolter and Maloof, 2006. [Eric Drucker][4-15] {download}
  • "A machine learning approach to POS tagging" Marquiz et al., 2000. [Jiayun Han][4-16]{download}
  • "YALE: Rapid Prototyping for Complex Data Mining Tasks" Mierswa et al., 2006. [Muthukumaran][4-16] {download}
  • "Bayesian Optimization Algorithm, Population Sizing, and Time to Convergence" Pelikan et al., 2000.[Karan Sharma][4-16] {download}
  • "New Lower Bounds for the Snake-In-The-Box Problem: Using Evolutionary Techniques to Hunt for Snakes" Casella and Potter, 2004. [Kartheek][4-17]{download}
  • "Learning user interaction models for predicting web search result preferences" Agichtein et al., 2006. [Amir H. Asiaee][4-17]{download}
  • "Learning User Preferences for Sets of Objects" Marie des Jardins et al.,2006. [Bijaya Rath][4-17]{download}

    The course syllabus is a general plan for the course; deviations announced to the class by the instructor may be necessary.

  • Last modified: April 23, 2008.

    Khaled Rasheed (khaled[at]