CSCI/ARTI 8950 Machine Learning

Spring 2011: Tuesdays and Thursdays 3:30pm - 4:45pm & Wednesdays 3:35pm - 4:25pm, Boyd GSRC 306

Instructor: Prof. Khaled Rasheed
Telephone: (706)542-3444
Office Hours: Tuesday: 1:50-3pm and Wednesday: 4:35-6:00pm or by email appointment
Office Location: Room 219B, Boyd GSRC
Email: khaled@cs.uga.edu

Objectives:

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, bioinformatics, biomedical modeling, 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: 20% (Programs, homeworks, attendance, paper presentation)

Midterm Examination: 25%

Final Examination: 25%

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 http://www.cs.uga.edu/~khaled/MLcourse/, 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

Announcements:

[5-3-2011] The final exam will be next Tuesday, 5-10-2011 from 3:30 pm to 6:30 pm in the same room where the class met during the semester. The exam will be open book and notes. It will cover all the material covered in the course including all handouts but more emphasis will be on the topics not covered by the midterm. You should bring your lecture notes, handouts and any books or notes you anticipate using in the exam. The use of cell phones, laptops or any computers or communication devices will not be allowed in the exam. One paper from among those presented by students in class will be selected at random to become the subject of one question in the final. Copies of that paper will be provided for all of you and therefore you need not bring copies of any or all of those papers to the exam.

[5-3-2011] The course project reports are due at the final exam. For the project report format, please write it as a conference paper of about 8 two-column pages or 12 single-column pages (there is no restriction on size though). You should include an introduction, a mention of related work if any, a description of your experiments and results and a conclusion. In the introduction or elsewhere in the paper you should describe the domain that you applied your ML technique(s) to, in enough detail for the reader to appreciate the significance and difficulty of the problem. Please bring a hard copy to the final and include your email addresses as well as the URLs of any demo/supporting web pages. There is a slight chance that I might contact you soon after the submission deadline (within 48 hours) requesting codes, clarifications or more data. Thus it will be helpful to include the emails of all the group members lest one or more are going to leave town immediately after the submission.

[2-3-2011] My former student Cesar Koirala has kindly prepared a power point presentation about Weka that you can find HERE. This gives step-by-step instructions on how to use the package for your homework assignments and provides a complete tutorial.

Papers

"Action Recognition by Learning Mid-level Motion Features" 2008. [Sherrene][3-30] {download}

"Online Learning of Spacecraft Simulation Models" 2009. [Shima][3-30] {download}

"Trust-Based Classifier Combination for Network Anomaly Detection" 2008. [Babak][3-30] {download}

"Privacy-Preserving Data Mining: Why, How, and When" 2005. [I-Cheng][4-6] {download}

"Spam Filtering with Naive Bayes Which Naive Bayes?" 2006. [Arsham][4-6] {download}

"Human Activity Recognition with Metric Learning" 2008. [Anirban][4-6] {download}

"Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs" 2009. [Deepika][4-13] {download}

"A Tool for Measuring the Reality of Technology Trends of Interest" 2009. [Raga][4-13] {download}

"Cross-Platform Comparison of Microarray-Based Multiple-Class Prediction" 2011. [Ahmad][4-13] {download}

"Sentiment classification of online reviews to travel destinations by supervised machine learning approaches" 2009. [Shayi][4-19] {download}

"Classification of MMPI Profiles of Patients with Mental Disorders Experiments with Attribute Reduction and Extension" 2010. [Tyler][4-19] {download}

"A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification" 2008. [Andrew][4-19] {download}

"A review of feature selection techniques in bioinformatics" 2007. [Ben][4-19] {download}

"Stochastic modeling western paintings for effective classification" 2007. [Casey][4-20] {download}

"Spatial and anatomical regularization of SVM for brain image analysis" 2010.[Fan][4-20] {download}

"Financial market forcasting using a two step kernel learning method for the support vector regression" 2010.[Ganesh][4-20]{download}

"Real-Time Human Pose Recognition in Parts from Single Depth Images" 2011.[Giva][4-21]{download}

"Learning Fast Classifiers for Image Spam" 2007. [Mehdi][4-21] {download}

"Evolving Combat Algorithms to Control Space Ships in a 2D Space Simulation Game with Co-evolution using Genetic Programming and Decision Trees" 2008. [Allen][4-21] {download}

"Clustering by Passing Messages Between Data Points" 2007. [Lim][4-21] {download}