CSCI/ARTI 8950 Machine Learning

CSCI/ARTI 8950 Machine Learning

Spring 2017: Mondays 3:35pm - 4:25pm Pharmacy Building Room 362 & Tuesdays and Thursdays 3:30pm - 4:45pm, Boyd GSRC Room 306

Instructor: Prof. Khaled Rasheed
Telephone: (706)542-0881
Office Hours: Monday: 2:00-3:00pm and Thursday: 2:00-3:00pm or by email appointment
Office Location: Room 111, 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 4380/6380 Data Mining or 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, ensemble 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:

    Attendance; reading; assignments (some include programming and/or running existing programs); midterm exam; and term project and paper.

    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.

    Group Study Policy:

    Group study is a powerful resource for graduate students and is therefore encouraged. During group study, you may discuss in detail any homework problems. However, you must write or type your homework on your own. Furthermore, you should never look at or copy a complete solution to a problem from another student's homework or allow another student to look at or copy a complete problem solution from your homework. Finally, you should acknowledge group study by listing the names of the students you studied with at the beginning or end of your homework. Participation in group study is optional and will not affect your grade in any way.

    Grading Policy:

  • Assignments: 25% (Programs, homeworks, attendance, paper presentation)
  • Midterm Examination: 25%
  • Term Project: 50% (includes term paper and presentation)
    Students may work on their term projects in groups of up to four 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 (3rd edition)", Ian Witten & Eibe Frank. Morgan Kaufmann, 2010.
  • "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
  • The Kaggle data science home


  • [4-17-2017] The course project presentations will be on Tuesday 5-2-2017 from 3:30 pm to 6:30 pm in the same room where the class met during the semester. Please plan for a 15 minute presentation per project group, focusing on the results. All members of the project group should take part in the presentation.
  • [4-17-2017] The course project reports are due by email on Tuesday, 5-5-2017 by 5 pm. 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 a title, an abstract, 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 also 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.
  • [3-11-2017] The midterm exam will be this upcoming Thursday 3-16-2017 in class 3:30-4:45pm. It will cover all the topics discussed in the course up to the end of Chapter 5, including all handouts related to Chapter 5 or earlier chapters. It will be open notes but the use of books or laptops will not be allowed. You should bring your lecture notes and all handouts and you are encouraged to also bring any additional notes, homeworks etc.


  • "Memory-based context-sensitive spelling correction at web scale" A Carlson and I Fette, 2007. [Chen, Ge][3-30] {download}
  • "FAST COMMUNITY DETECTION BY SCORE" 2012. [Jiankun, Xiaodong][4-10] {download}
  • "Unsupervised Clustering-based SPITters Detection Scheme" 2015. [Liu, Xu][4-13] {download}
  • "Latent Dirichlet Allocation" 2003. [Cali, Taylor][4-13] {download}
  • "Privacy-preserving logistic regression" 2008. [Saeid, Jonathan][4-17] {download}
  • "Why didn’t my (great!) protocol get adopted?" 2015. [Mehdi, Abolfazl] [4-18] {download}
  • "Prediction and Prioritization of Rare Oncogenic Mutations in the Cancer Kinome Using Novel Features and Multiple Classifiers" ManChon U et. al., 2014. [Zengya,Jin][4-18] {download}
  • "Recognizing Functions in Binaries with Neural Networks" 2015. [Aditya, Kevin] [4-18] {download}
  • "Reinforcement Learning Algorithms for solving Classification Problems" 2010. [Anuja, Shubha][4-20] {download}
  • "Markov games as a framework for multi-agent reinforcement learning" 2007. [Keyang, Shibo][4-20] {download}
  • "A survey on feature selection methods" 2014. [Raunak,Parya][4-20] {download}
  • "CricAI: A Classification Based Tool to Predict the Outcome in ODI Cricket" 2010. [Adithya, Amitabh][4-24] {download}
  • "Wheat yield prediction using machine learning and advanced sensing techniques" 2016. [Brad, Shangpeng][4-24] {download}
  • "An empirical study of three machine learning methods for spam filtering" C. Lai., 2007. [Sam,Andrew][4-25] {download}
  • "Thumbs up?: sentiment classification using machine learning techniques" 2002. [Hamed][4-25] {download}
  • "A review of feature selection techniques in bioinformatics" 2007. [?,?][?] {download}
  • "Clustering by Passing Messages Between Data Points" Brendan J. Frey and Delbert Dueck, 2007. [?,?][?] {download}
  • "Support Cluster Machine" bin Li et al., 2007. [?,?][?] {download}
  • "OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction" 2009. [?,?][?] {download}
  • "Good Learners for Evil Teachers" 2009. [?,?][?] {download}
  • "A semantic Bayesian network approach to retrieving information with intelligent conversational agents" K. Kim et al., 2007.[?][?] {download}
  • "Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs" 2009. [?,?][?] {download}


  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Assignment 4
  • Assignment 5
  • Assignment 6

    Lecture Notes:

  • Introduction
  • Chapter 1
  • Chapter 2
  • Chapter 3
  • Chapter 4
  • Weka Introduction 1
  • Weka Introduction 2
  • Weka Introduction 3
  • Chapter 5
  • Chapter 6
  • Chapter 8
  • Chapter 7
  • Deep Learning Guest Lecture
  • Evolutionary Computation
    The course syllabus is a general plan for the course; deviations announced to the class by the instructor may be necessary.

    Last modified: April 17, 2017.

    Khaled Rasheed (