Course Syllabus -- EEL 4817 (H)
Current Topics in Machine Learning II
Spring 2008
Textbook: There are no lecture notes or textbook associated with this class. Based on the
background material of current research results in Machine Learning that the students have been
exposed to in Machine Learning I class (EEL 4818(H), taught in the Fall of 2006), the students
will be assigned research projects to work on. The projects will be weekly supervised by the
faculty, assigning the project, and on a more frequent basis by their graduate students. All
students will report progress of their work monthly. The faculty involved in this teaching effort
will be: Georgiopoulos (SEECS), A. Gonzalez (SEECS), Ivan Garibay (SEECS), and A. S. Wu
(SEECS).
Instructors:
Michael Georgiopoulos (michaelg@mail.ucf.edu)
A. S. Wu (aswu@cs.ucf.edu)
Ivan Garibay (igaribay@mail.ucf.edu)
Avelino Gonzalez (gonzalez@pegasus.cc.ucf.edu)
Targeted Students: Honors students who have taken EEL 4818 (H) (Machine Learning I) in the
Fall 2007 are encouraged to take this class. Honors students who have not taken EEL 4818 (H) in
the Fall of 2007 can register for EEL 4817(H) (Machine Learning II) as well, since Machine
Learning II is a self-contained class. Students who are not Honors students can also register for
this class by e-mailing Dr. Georgiopoulos, and after they are given permission to register by the
Honors College. Note that if you are an undergraduate student this is an excellent opportunity to
know some of the professors in the SEECS in case you have aspirations to pursue a graduate
degree at UCF as an MS or a BS+MS student.
Other Prerequisites: EEL3801 or COP3032, or STA3032, and ML-I (EEL 4817(H)). The
prerequisite of ML-I (EEL 4818(H)) can be waived by e-mailing Dr. Georgiopoulos and
explaining to him your academic background; decisions to waive EEL4818(H) will be made on
an individual basis.
Goal: The goal of this class is to introduce undergraduate students in the CECS to the current
research results in Machine Learning. After having exposed the students in ML-I to current
research topics of Machine Learning that are of interest to the faculty teaching this class, the
students will be assigned appropriate research projects. The students will work on these projects
under faculty supervision and under the faculty’s graduate student supervision. The goal is to be
able to publish the research findings in conferences and hopefully journals.
Course Outline:
There is no course outline associated with this class. Examples of potential projects that could
assigned to students are listed below. Note that these are simply examples of projects and they do
not necessarily represent the projects that could be assigned to students that will take EEL
4817(H).
Sample Project 1: Comparison of Fuzzy ARTMAP and Bayesian Classifiers.
Project Description: Fuzzy ARTMAP is a neural network architecture introduced by Carpenter,
et al., 1992. Since its inception it has been proven to be one of the premier neural network
architectures for classification problems. For example, in a fairly recent publication Joshi, et al.
(see Joshi, 1997), compared more than 20 classifiers on 7 different machine learning problems.
The conclusion of this study was that Fuzzy Min-Max (see Simpson, 1992) that belongs to the
same family of networks as Fuzzy ARTMAP gives the best or the second best classification
accuracy over all the other algorithms on these machine learning problems. Some of the
advantages that Fuzzy ARTMAP has, compared to other neural network classifiers, is that it
learns the required task fast, it has the capability to do on-line learning, and its learning structure
allows one to explain the answers that the neural network produces. On the other hand, Bayesian
learning algorithms that explicitly calculate the necessary probabilities to build the classifier are
amongst the most practical approaches to certain types of learning problems. For example,
Michie, et al, (1994) and Kohavi (1995) provided a detailed comparison of the naïve Bayes
classifier to other machine learning algorithms including decision trees and neural networks (not
Fuzzy ARTMAP). These researchers showed that the naïve Bayes classifier is competitive with
these other learning algorithms, and in some cases it outperforms these other methods. Therefore
a comparison between these two very competitive classifiers is worth pursuing. The comparison
will first focus on artificially generated data. One of the advantages of working with artificial
databases first is that we can produce as many data-points as we want. Another advantage of the
artificially generated data is that we can experiment with different values for the dimensionality
of the input space of the pattern classification task, different values of the number of output
categories that the data belong to, and different degrees of overlap amongst the data that belong to
different classes. Our belief is that the comparison results will be dependent on all of these
variables. Once our comparison is complete for the artificially generated data we will extend the
work to real databases. An important part of this work is to meaningfully design the experiments
so that we can draw some useful conclusions from this effort. An essential element of this effort
is also to establish ways of correctly assessing the performance of the Bayes classifier and the
Fuzzy ARTMAP classifier. One measure of comparison is the classification accuracy of the
classifiers on a data set that is called test set (different than the training set). Other measures of
comparison are the required memory needed to build the Bayes or the Fuzzy ARTMAP
classifiers, and the computations needed by each classifier to produce a response to a specific
input. A pertinent subject associated with this research effort is how confident we are in the
assessments that we make about the goodness of each one of these algorithms. Ways of making
confident assessments about the goodness of algorithms is extensively discussed in Dietrrich
(1998) and in a Machine Learning book by T. M. Mitchell (see Mitchell, 1997, "Evaluating
Hypotheses" chapter 5).
Knowledge that the student will be exposed to in this project: The student who will be
involved in this project will be exposed to the following papers and book material: Fuzzy
ARTMAP (see Carpenter, 1992), Bayes Classifier (see Mitchell, 1997, Bayesian Learning
chapter 6), estimating the probabilities associated with the Bayes classifier (see Specht, 1992),
evaluating hypotheses (see Mitchell, 1997, Evaluating Hypotheses chapter 5), and others. The
faculty and his/her graduate student involved with this project will help the UG student
understand this material. The student will also be involved in coding the Bayes classifier. The
code of the Fuzzy ARTMAP network will be provided to the student.
Expected Results: The results of this effort will be a detailed comparison of 2 powerful
classifiers and associated conclusions that will help researchers in the field choose one classifier
versus the other. The results of this work will be published in journals and conferences.
Sample Project 2: Genetic Algorithm Visualization
Project Description: Traditional methods of evaluating genetic algorithm (GA) performance
focus on the average time to find a solution or the average quality of solutions found over
multiple runs. While such measures can provide estimates of the expected performance of a
particular GA configuration, they do not reveal very much information on how a GA found a
solution. To fully understand how a GA works, it is important to understand both the expected
end and the means to the end. The large numbers of complex, non-linear interactions that
compose GAs make them difficult to analyze and a challenge to understand. While all of the data
from a GA run can easily be saved into files, accessing and interpreting such a large amount of
information is no trivial task. The development of tools for sorting, organizing and displaying
such databases could greatly facilitate the access and analysis of such data.
Graphical visualization techniques are some of the simplest and at the same time most powerful
methods for analyzing and communicating information (Tufte 1983). Well-designed graphical
elements can convey large amounts of information in very concise and compact formats. In
addition, the human vision system is extremely sensitive to graphical patterns, making graphical
representations an extremely useful analysis tool. We are in the process of designing an offline
visualization tool to aid in analysis of GA behavior (Wu, et al. 1999). Our system takes advantage
of the effectiveness of graphical representations and the flexibility of "clickable'' links to provide
a navigation tool for accessing and viewing data from a GA run.
Knowledge to which the student will be exposed to in the project: The UG student will work
closely with the faculty and with graduate students involved with this project to determine useful
methods for displaying GA data from ongoing projects. The UG student will gain an
understanding of the basic GA, learn basic computer graphics programming methods, and
become familiar with basic scientific data visualization techniques.
Expected Results: The UG student will develop visualization techniques and add them to an
existing basic visualization program. Techniques will include display of individual solutions as
well as data collection/display over a population. The UG student's work on this project will
benefit all ongoing projects in the PI's laboratory by providing new methods for evaluating and
understanding individual GA runs. In addition, there are plans to publish a paper describing the
complete GA visualization system as well as making it available for other researchers in the GA
community to use.
Meeting Schedule:
Each one of the instructors in the class will have the opportunity to present to class a few of the
machine learning research projects that he/she is interested in involving the EEL 4817 (H)
students with. As of now, the schedule is as follows:
Lecture 1: Georgiopoulos (SEECS)
Lecture 2: Garibay (SEECS)
Lecture 3: Gonzalez (SEECS)
Lecture 4: A. S. Wu (SEECS)
It is expected that by the third week of January, each one of the students in this class would have
picked up a project to work on. After January, each student(s) will interact with the individual
professor who is sponsoring the project, and with the professor’s graduate students. After
January, at the third week of every month, we will meet as a group (all PIs, all Machine Learning
II students, and potentially graduate students) to discuss the progress of the work. We anticipate
having 3 group meetings of this nature.
At the end of the semester the students will submit a typed report that explains in detail the
project that they have worked on, the results that they have produced and discussion of these
results. It is anticipated that an effort will be made for all, or most, of the reports to be submitted
for publication in conferences and journals.
Every professor might have his or her individual preferences on what the report should contain.
The professor who sponsors the machine-learning project will also grade the report. An example
of the specific contents within a report are: (a) an introduction/problem statement, (b) main body
where you address all the tasks (in case the project is broken down in individual tasks), you
discuss the neural network classifiers (in your own words) and elaborate on their
differences/similarities, (c) Experiments/Results portion (where you discuss the data that you
have generated and experimented with and the derived results), and (d) finally a conclusions
section where you summarize your conclusions. Create tables and figures wherever appropriate in
your report.
Your grade will depend on completeness of your work, conciseness and clarity of your work and
innovativeness.
Course Materials:
Course materials will be provided by the supervisor of each project to the student.
References:
Carpenter, G. A., Grossberg, S., and Reynolds, J. H., “ARTMAP: Supervised real-time learning
and classification of non-stationary data by a self-organizing neural network,” Neural Networks,
Vol. 4, pp. 565-588, 1991b.
Carpenter, G. A., Grossberg, S., and Rosen, D. B., “Fuzzy ART: Fast stable learning and
categorization of analog patterns by an adaptive resonance system, ” Neural Networks, Vol. 4,
No. 6, pp. 759-771, 1991c.
Carpenter, G. A., Grossberg, S., and Reynolds, J.H., "Fuzzy ARTMAP: A neural network
architecture for incremental supervised learning of analog multi-dimensional maps," IEEE
Transactions on Neural Networks, Vol. 3, No. 5, pp. 698-713, 1992.
Dieterich, T. G., "Approximate statistical test for comparing supervised classification learning
algorithms," Neural Computation, Vol. 10, No. 7, pp. 1895-1923, 1998.
Joshi, J., Ramakrishnan, N., Houstis, E.N., and Rice, J.R., "On neurobiological, neuro-fuzzy,
machine learning, and statistical pattern recognition techniques, " IEEE Transactions on Neural
Networks, Vol. 8, No. 1, pp. 18-31, 1997.
Kohavi, R., Becker B., and Sommerfield, D., "Improving the Simple Bayes," The 9th European
Conference on Machine Learning, 1997.
Michie, D., Spiegelhalter, D. G., and Taylor, C.C., Machine Learning, neural and statistical
classification, (edited collection). New York: Ellis Horwood, 1994.
Mitchell, T., M., Machine Learning, McGraw-Hill Companies, Inc., 1997, New York.
Simpson, P. K. "Fuzzy min-max neural networks -- Part 1: Classification", IEEE Transactions on
Neural Networks, Vol. 3, No. 5, pp. 776-786, Sept. 1992.
Tufte E. R. The Visual Display of Quantitative Information, Graphics Press, 1983.
Wu, A. S., De Jong, K. A., Burke, D. S., Grefenstette, J.J., and Ramsey, C.L., "Visual analysis of
evolutionary algorithms", In Proceedings of the 1999 Congress on Evolutionary Computation,
1999.
Grading:
Project Assignment: 100% (The grading of the project will be based on the frequent interactions
that the faculty and the students have during the project completion process and a detailed typed
project that the students have to submit at the end of the class).
The letter grade policy is as follows:
A: 90 and above
B: 80-89
C: 70-79
D: 60-69
F: < 60
Note that the aforementioned guidelines are approximate and it is up to the instructors’
discretion to change them, provided that prior notice is given to you.