University of Florida
Department of Electrical and Computer
EEL 6825, Section 026A
This is a 3-credit course.
The objective of this course is to impart a working knowledge of several
important and widely used pattern recognition topics to the students through a
mixture of motivational applications and theory.
- Richard O. Duda, Peter E. Hart, David G.
Stork, ``Pattern Classification,'' 2nd Edition, Wiley-Interscience,
October 2000. ISBN-10: 0471056693 | ISBN-13:
- David G. Stork, Elad Yom-Tov, ``Computer
Manual in MATLAB to accompany Pattern Classification," 2nd Edition,
Wiley-Interscience, April 2004. ISBN:
- Christopher M. Bishop, “Pattern Recognition and Machine Learning”, 1st
October 1, 2007. ISBN-10: 0387310738 |
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, "The
Elements of Statistical Learning: Data Mining, Inference, and Prediction,"
Second Edition, Springer,
February 9, 2009. ISBN-10: 0387848576 | ISBN-13: 978-0387848570.
- Kevin Patrick
Learning: a Probabilistic Perspective," the MIT Press,
August 24, 2012. ISBN-10: 0262018020 | ISBN-13: 978-0262018029.
Dr. Dapeng Wu
Office: NEB 431
1) Qi Cai
2) Chiranjib Sur
Monday, Wednesday, Friday, period 7 (1:55 pm -
- Dr. Wu: Monday, Wednesday, period 9 (4:05 pm -
and by appointment
Structure of the Course
The course consists of lectures, 4 homework
assignments, and 1 project.
This course is primarily a lecture course. I cover all important
material in lectures. Since EEL 3135 and EEL 4516 are
prerequisites, I assume some previous knowledge about DSP, probability theory
and stochastic processes, and hence I will cover some material very quickly.
Thus, depending on what and how much you recall from earlier study, varying
amounts of reading in introductory books on DSP, probability theory and
stochastic processes (other than the course textbook) may be necessary; these readings are up to the student.
I will only give reading assignments from the course textbook.
Attending lecture is quite important as I may cover material not available in
any book easily accessible to you. I use Powerpoint presentation during lecture. Lecture
notes will be posted on the course website before the class. The lecture
is to engage the students in independent thinking, critical thinking, and
creative thinking, help the students organize the knowledge around essential
concepts and fundamental principles, and develop conditionalized knowledge
which tells them when, where and why a certain method is applicable to solving
the problem they encounter.
I do not intend for the WWW material to be a substitute for attending lecture
since engaging the students in active thinking, making logical connections
between the old knowledge and the new knowledge, and providing insights are the
objectives of my lecture. The lecture notes are posted on the web so
that you can miss an occasional lecture and still catch up, and it makes taking
notes easier. To reward those who attend regularly, there will be some
lecture-based material in the exam which is not available via the web.
The class project is described here.
- Bayesian decision theory
- Parametric estimation and supervised learning
- Nonparametric methods
- Linear discriminant functions
- Unsupervised learning and clustering
- Nonmetric methods
- Feature extraction and feature selection
Upon the completion of the course, the student should be able to
- use the fundamental techniques for pattern recognition
- understand the basics of statistical learning theory
- acquire the basic skill of designing machine learning algorithms and
Please find handouts here.
- Perfect class attendance is not required, but regular attendance is
- It is the student's responsibility to independently obtain any missed
material (including handouts) from lecture.
- During lecture, cell phones should be turned off.
- No late submissions of your homework solution, and project proposal/report, are allowed
unless U.F. approved reasons are supplied and advance permission is granted by
the instructor. Excused late submissions are consistent with university
policies in the undergraduate catalog (https://catalog.ufl.edu/ugrad/current/regulations/info/attendance.aspx)
and require appropriate documentation.
faculty, staff and student of the University are required and expected to obey
the laws and legal agreements governing software use. Failure to do so can
lead to monetary damages and/or criminal penalties for the individual
violator. Because such violations are also against University policies and
rules, disciplinary action will be taken as appropriate. We, the members of
the University of Florida community, pledge to uphold ourselves and our peers
to the highest standards of honesty and integrity.
- All students are responsible for announcements made in lecture, on the
student access website, or via the class email list.
- It is expected that you will check your email several times per week for
possible course announcements.
- Students with disabilities:
- Students with disabilities requesting accommodations should first
register with the Disability Resource Center (352-392-8565, https://www.dso.ufl.edu/drc)
by providing appropriate documentation. Once registered, students will
receive an accommodation letter which must be presented to the instructor
when requesting accommodation. Students with disabilities should follow this
procedure as early as possible in the semester.
UF students are bound by The Honor
Pledge which states, “We, the members of the University of Florida community,
pledge to hold ourselves and our peers to the highest standards of honor and
integrity by abiding by the Honor Code. On all work submitted for credit by
students at the University of Florida, the following pledge is either required
or implied: “On my honor, I have neither given nor received unauthorized aid
in doing this assignment.” The Honor Code (https://www.dso.ufl.edu/sccr/process/student-conduct-honor-code/)
specifies a number of behaviors that are in violation of this code and the
possible sanctions. Furthermore, you are obligated to report any condition
that facilitates academic misconduct to appropriate personnel. If you have any
questions or concerns, please consult with the instructor or TAs in this
Students are encouraged to discuss
class material in order to better understand concepts. All homework answers
must be the author's own work. However, students are encouraged to discuss
homework to promote better understanding. What this means in practice is that
students are welcome to discuss problems and solution approaches, and in fact
can communally work solutions at a board. However, the material handed in must
be prepared starting with a clean sheet of paper (and the author's
recollection of any solution session), but not refer to any written notes or
existing code from other students during the writing of the solution. In other
words, writing the homework report shall be an exercise in demonstrating the
student understands the materials on his/her own, whether or not help was
provided in attaining that understanding.
All work submitted in this course must be your own and produced exclusively
for this course. The use of sources (ideas, quotations, paraphrases) must be
properly acknowledged and documented. For the copy of the UF Honor Code and
consequences of academic dishonesty, please refer to http://www.dso.ufl.edu/sccr/honorcodes/honorcode.php.
Violations will be taken seriously and are noted on student disciplinary
records. If you are in doubt regarding the requirements, please consult with
the instructor before you complete any requirement of the course.
Students are expected to provide feedback on the quality of instruction in
this course by completing online evaluations at https://evaluations.ufl.edu/evals.
Evaluations are typically open during the last two or three weeks of the
semester, but students will be given specific times when they are open.
Summary results of these assessments are available to students at
All faculty, staff, and students of the University are required and expected
to obey the laws and legal agreements governing software use. Failure to do so
can lead to monetary damages and/or criminal penalties for the individual
violator. Because such violations are also against University policies and
rules, disciplinary action will be taken as appropriate. We, the members of the
University of Florida community, pledge to uphold ourselves and our peers to the
highest standards of honesty and integrity.
There are federal laws protecting your privacy with regards to grades earned
in courses and on individual assignments. For more information, please see:
If you or a friend is in distress,
or 352 392-1575 so that a team member can reach out
to the student.
Counseling and Wellness Center:
392-1575; and the University Police Department: 392-1111 or 9-1-1 for
Recovery Services (SARS)
Student Health Care Center,
Police Department at 392-1111 (or 9-1-1 for emergencies), or
technical support, 352-392-4357 (select option 2) or
e-mail to Learningfirstname.lastname@example.org.
Center, Reitz Union, 392-1601. Career
assistance and counseling.
http://cms.uflib.ufl.edu/ask. Various ways
to receive assistance with respect to using the libraries or finding
Teaching Center, Broward Hall, 392-2010 or 392-6420. General study
skills and tutoring.
302 Tigert Hall,
846-1138. Help brainstorming, formatting,
and writing papers.
4pm, April 26
The project report consists of
- (50%) A written report for your project
- (25%) Computer programs that you develop for your
- (10%) Powerpoint file of your presentation
- (15%) Your presentation/demo video on
Top 25% students will receive A. Average score will be at least B+.
More information on UF grading policy may be found at: https://catalog.ufl.edu/ugrad/current/regulations/info/grades.aspx
- Due dates of assignments are specified in the
- No late
submissions are allowed unless U.F. approved reasons are supplied and
advance permission is granted by the instructor.
- If you wish to dispute a
homework grade, you must return the assignment along with a succinct written
argument within one week after the graded materials have been returned to the
class. Simple arithmetic errors in adding up grade totals are an exception,
and can normally be handled verbally on-the-spot during office hours of the
TA. For all other disputes, the entire homework may be (non-maliciously)
re-graded, which may result in increase or decrease of points.
The class project will be done individually.
Each project requires a proposal and a final report. The final report is expected to be
in the format of a conference paper plus computer programs, a Powerpoint
file, and a video.
3, the project proposal (up to 2 pages) is due. On April 26, the
final report (up to 10 pages) is due. For details about the project,
please read here.
Suggested topics for projects are listed here.
Course calendar can be found here.
- Anaconda: Anaconda is the
leading open data science platform powered by Python.
Theano is a Python library that lets you to define, optimize, and evaluate
mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
- Keras: Keras is a minimalist,
highly modular neural networks library, written in Python and capable of
running on top of either TensorFlow or Theano. It was developed with a focus
on enabling fast experimentation. Being able to go from idea to result with
the least possible delay is key to doing good research.
Source code in Python for handwritten digit recognition, using deep neural networks
Source code in Python for
TF-mRNN: a TensorFlow library for image captioning
Source code in Python for the following work:
- Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan, Show and
Tell: A Neural Image Caption Generator, CVPR 2015
Microsoft COCO datasets
Source code in Python for sequence-to-sequence learning (language translation,
Automatic text understanding and reasoning:
Matlab implementation of image/video compression algorithms
Introduction to Matarix Algebra (free book by Autar K Kaw, Professor,
University of South Florida).
- Matrix Reference
- HIPR2: a WWW-based Image
Processing Teaching Materials with J
- Learning by simulations
- Download the following
free (open source)
program to record video with screen capture:
- Virtual Dub: VirtualDub
is a video capture/processing utility for 32-bit Windows platforms
(95/98/ME/NT4/2000/XP), licensed under the GNU General Public License (GPL).
is an efficient multimedia viewer, browser and converter.
- ImageJ: Read and write GIF,
JPEG, and ASCII. Read BMP, DICOM, and FITS. [Open Source, Public Domain]
- Open source for image processing tasks:
- Computer Vision and
- Journal of Visual
Communication and Image Representation
- Data & Knowledge Engineering
- Image and Vision Computing
- Pattern Recognition
- Pattern Recognition Letters
- IEEE Transactions on
Circuits and Systems for Video Technology
- IEEE Transactions on Multimedia
- IEEE Transactions on
- IEEE Transactions on
- IEEE Transactions on PAMI
Public Domain Image Databases