YNG 138, Tue/Thur 2:00pm – 3:15pm






            Jinbo Bi

            Phone: 486-1458


            Office hours: Tue. 4:45pm – 5:30pm

            Office: ITEB 233





The purpose of this course is to introduce to the students the general topics and techniques of data mining and machine learning with specific application focus on biomedical informatics. This course introduces multiple real-world medical problems with real patient data, and how multiple analytic algorithms have been used in an integrated fashion to cope with these problems. It covers some cutting-edge data mining technology which can successfully tackle problems that are complex, highly dimensional, and/or ambiguous in labeling. General topics of data mining, such as clustering, classification, regression, dimension reduction, will be described. However, efforts will also be given to more advanced and recent topics. In particular, imprecisely supervised learning problems will be discussed, including multiple instance learning, metric learning, and learning with multi-labeler annotations etc.  Throughout the entire course, practical medical/healthcare problems will be used as examples to demonstrate the adoption and effectiveness of data mining methods.   

The course will consist of lectures, paper reviews and projects. Lectures will serve as the vehicle to introduce concepts and knowledge to students. Labs will be used to enforce the material given in lectures and students paper reviews will be used to study the state-of-the-art from researchers in the field. Participation is encouraged during the class. 

Students are encouraged to form study groups. Three study groups are expected, and each group will be assigned a machine learning topic to study. The team member of the study group will present a series of lectures to the class.  At the end of each topic study session, the study group who presents the topic will turn in their presentation slides. At the end of each study session, a short quiz will be given to test out the learning outcome. The instructor will guide through all study sessions.

As part of the course, the students will work on a term project with the goal of applying any of the studied techniques to a problem selected from a list of projects. Students are also encouraged to propose and design their own problems which need to be approved by the instructor for class suitability. Teams of two-three students will be created for each project. Each team is required to present in the classroom and submit a project report, of 10-20 pages, which includes the definition of the problem, techniques used to solve the problem and experimental results obtained. This exercise will help the team gain a hands-on understanding of the material studied in this course and promotes collaborations among team members.



  1. Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, ISBN-10: 0321321367
  2. Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart and David G. Stork, ISBN-10: 0471056693
  3. Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop, ISBN-10: 0387310738


  1. Micro teaching assignment (1): 20%
  2. In-class/In-lab open-book open-notes quizzes (5): 40%
  3. Term Project (1): a team can only consist of one or two persons, 30% (choosing project from a list of pre-defined ML projects)
  4. Participation: 10%


8 Lectures by the instructor

4-6 Lectures by topic study groups (three topics of this semester are:

            Support Vector Machines

            Spectral Clustering

            Boosting )

2 Invited Lectures on some application topics

2 Classes of Presentations on the progress of term projects

2-3 Classes of Final Presentations of term projects



1.      Computers are allowed in classroom for taking notes or any activity related to the current class meeting.

2.      Participation in microteaching sessions itself will earn 50% credits for each review assignment. Microteaching presentation slides need to be turned in via HuskyCT before the class that the presentation is scheduled.  The quality of your presentation slides will be judged by the instructor and constitutes the other 50% credits.

3.      Quizzes will be graded by the instructor.

4.      Final term projects will be graded by the instructor based on the clarity and creativity of the project report and the comparison of final presentation of all teams.


1.      If you cannot attend a teaching session that you are supposed to present, you need to find your team member to cover you.

2.      If a quiz is missed, it should be fine because we drop your lowest quiz score in the final score calculation.  If you miss two quizzes, there will be a take-home quiz to make up the credits.


A HuskyCT site has been set up for the class. You can access it by logging in with your NetID and password.  You must use HuskyCT for submitting assignments.  The instructor uses the HuskyCT announcement to announce class materials, grades, problem clarifications, changes in class schedule, and other class announcements.



We study the three main topics from the following tutorials

Support vector machines

Spectral clustering

Boosting 1

Boosting 2


The three groups of students have been formed, and please see here for the list.


Tools that may help with course projects (to be complete)

  1. Matlab Optimization Toolbox
  2. SVM_Light (support vector machines)
  3. LIBSVM (support vector machines)
  4. Bayesian Knowledge Discoverer (BKD): computer program able to learn Bayesian Belief Networks from databases
  5. Bayes net toolbox for Matlab
  6. TSP Demo
  7. LeNet (neural networks)
  8. Neural networks demo
  9. Neural networks flash demo
  10. GAUL (genetic algorithm)
  11. Java genetic algorithm demo
  12. A complete notebook GA
  13. A system for distributing statistical software, datasets, and information by electronic mail, FTP and WWW
  14. Tools for mining large databases C5.0 and See5
  15. Description of the SLIPPER rule learner, that is a system that learns sets of rules from data based on original RIPPER rule learner
  16. Information about Data Mining and knowledge discovery in Databases
  17. Clustering Algorithms


You are expected to adhere to the highest standards of academic honesty. Unless otherwise specified, collaboration on assignments is not allowed. Use of published materials is allowed, but the sources should be explicitly stated in your solutions. Violations will be reviewed and sanctioned according to the University Policy on Academic Integrity. Collaborations among team members are only allowed for the final term projects that are selected.

“Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations, facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work for another person or work previously used without informing the instructor, or tampering with the academic work of other students.”


If you have a documented disability for which you are or may be requesting an accommodation, you are encouraged to contact the instructor and the Center for Students with Disabilities or the University Program for College Students with Learning Disabilities as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

Jinbo Bi ©2014/8-2014/12
Last revised: 8/23/2014