Machine Learning Course (EEL 4818(H))
Fall Semester 2007
Monday-Wednesday-Friday (12:30 p.m. – 1:20 p.m.)
Objectives
Machine Learning (ML) is a discipline that started evolving as early as the 60’s from work in the field of
Artificial Intelligence. The course’s charter is to study, develop and build models able to perform
“intelligent” tasks that may be second nature for humans, but are well beyond the capabilities of traditional
computing paradigms. ML draws its background from a wide, multidisciplinary base that includes
Mathematics (classical/combinatorial optimization, game theory, etc.), Statistics (statistical inference,
detection and estimation, etc.), Engineering (information theory, signal processing, simulation & modeling,
etc.), as well as Computer Science (algorithmic analysis, optimal software implementations, computer
vision, etc.), Physics and Biology (non-traditional computing paradigms, such as simulated annealing,
quantum computing, genetic algorithms, swarm intelligence, etc.). The objective of this course is to provide
students with the fundamental concepts in the field of Machine Learning that will help them understand
popular ML algorithms and the theory behind them. Furthermore, a variety of applications, where ML
algorithms have proven their usefulness will be emphasized. The applications will be drawn from fields
such as Electromagnetics, Digital Signal Processing, Computer Vision, Remote Sensing, Communications,
to mention only a few.
This class will serve as a prelude of another Machine Learning class, that is scheduled to be taught in the
Spring of 2008, called Machine Learning II (EEL 4817). Machine Learning II is a project-oriented class
and its purpose is to give the opportunity to interested students to get involved with a Machine Learning
project. The work that a student does in the Machine Learning II class could potentially lead into a
publication to an appropriate conference venue.
Instructors
The instructors in this class are professors in the School of EECS, and they are listed below:
Michael Georgiopoulos (michaelg@mail.ucf.edu)
Avelino Gonzalez (Gonzalez@pegasus.cc.ucf.edu)
A. S. Wu (aswu@cs.ucf.edu)
Ivan Garibay (igaribay@mail.ucf.edu)
The more detailed schedule for these instructors is as follows:
Lectures from August 20th to October 19th: Michael Georgiopoulos
Lectures of October 22, 24, 26, 29, 31 and November 2: Annie S. Wu
Lectures of November 5, 7, 9, 14 and 16: Ivan Garibay
Lectures of November 19, 21, 26, 28, 30 and December 3: Avelino Gonzalez
Textbook
There is no textbook for this class. Instructors will provide notes, and papers related to the material taught
in the class. Note that the material covered in this class is part of the research that these professors have
done (are doing) in the field of Machine Learning and as a result, they are experts on the topic that they
intend to cover in this class.
Prerequisites
The class is open to UCF juniors and seniors from engineering and science disciplines with an interest in
Machine Learning. The students who take this course need to have a good understanding of Mathematics
covered in the sequence of the Calculus classes, and other math classes that they take in their first two years
in college. Furthermore, the students need to have some knowledge of a high level programming language
such as C, C++, or MATLAB.
Topics Covered in this class:
Introduction to Machine Learning, Clustering Algorithms, ART Neural Networks, Decision Trees, Support
Vector Machines, Genetic Algorithms, Co-Evolution, and Reinforcement Learning.
More Detailed Topics Covered in the Class:
1. Introduction to Machine Learning (M. Georgiopoulos)
2. Feed-Forward Neural Networks (M. Georgiopoulos)
 Basic Concepts
 The MLP Neural Network
 The Back-propagation Algorithm
 Applications
3. ART Neural Networks (M. Georgiopoulos)
 Basic Concepts
 The Fuzzy ARTMAP Neural Networks
 ART Neural Network Properties
 Applications
4. Decision Trees (M. Georgiopoulos)
 Basic Concepts
 Growing Phase
 Pruning Phase
 Classify Phase
 Applications
5. Support Vector Machines (M. Georgiopoulos)
 Basic Concepts
 The SMO Algorithm
 Other SVM algorithms
 Applications
6. Genetic Algorithms (A. S. Wu)
 Problem Representation
 Fitness Evaluation
 Genetic Operators
 Selection Methods
 Implementation Issues
7. Co-Evolution (Ivan Garibay)
8.
Reinforcement Learning (A. Gonzalez)
Grading Scheme
A variety of homework exercises will assigned throughout the semester. A homework assignment should
be expected after every major topic (e.g., ART Neural Networks) has been covered. During finals week, a
final exam will be given that will test the student’s knowledge on the entire material covered in the class.