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.
Knowledge of basic
matrix theory (linear algebra) would be helpful, but not necessary
Required Textbook
Richard O. Duda, Peter E. Hart, David G.
Stork, ``Pattern Classification,'' 2nd Edition, Wiley-Interscience,
October 2000. ISBN-10: 0471056693 | ISBN-13:
978-0471056690.
Christopher M. Bishop, “Pattern Recognition and Machine Learning”, 1st
Edition, Springer,
October 1, 2007. ISBN-10: 0387310738 |
ISBN-13: 978-0387310732.
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.
Eugene Charniak, "Introduction
to Deep Learning," the MIT Press,
ISBN: 9780262039512 (This book is a
project-based guide to the basics of deep learning)
Monday, Wednesday, Friday, period 8 (3:00 pm -
3:50 pm)
Meeting Room
Online
Office Hours
Dr. Wu: Monday, Wednesday, period 9 (4:05 pm -
4:55 pm),
and by appointment
via email.
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.
Perfect class attendance is not required, but regular attendance is
expected.
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.
Software use
All
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.
Announcements:
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.
University
Honesty Policy
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
class.
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.
Course Evaluation
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
https://evaluations.ufl.edu/results/.
Software Use
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.
Student Privacy
There are federal laws protecting your privacy with regards to grades earned
in courses and on individual assignments. For more information, please see:
http://registrar.ufl.edu/catalog0910/policies/regulationferpa.html
Online Course Recording
Our class sessions may be audio visually recorded for students in the class
to refer back and for enrolled students who are unable to attend live. Students
who participate with their camera engaged or utilize a profile image are
agreeing to have their video or image recorded. If you are unwilling to consent
to have your profile or video image recorded, be sure to keep your camera off
and do not use a profile image. Likewise, students who un-mute during class and
participate orally are agreeing to have their voices recorded. If you are not
willing to consent to have your voice recorded during class, you will need to
keep your mute button activated and communicate exclusively using the "chat"
feature, which allows students to type questions and comments live. The chat
will not be recorded or shared. As in all courses, unauthorized recording and
unauthorized sharing of recorded materials is prohibited.
Attendance Policy, Class Expectations, and Make-Up Policy
This class will be presented online using Zoom and requires access to a
working webcam and stable internet connection.
Excused absences must be in compliance with university policies in the
Graduate Catalog (http://gradcatalog.ufl.edu/content.php?catoid=10&navoid=2020#attendance)
and require appropriate documentation.
Campus Resources:
Health and
Wellness
U Matter,
We Care:
If you or a friend is in distress,
please contact umatter@ufl.eduor 352 392-1575 so that a team member can reach out
to the student.
Counseling and Wellness Center:
http://www.counseling.ufl.edu/cwc,
and
392-1575; and the University Police Department: 392-1111 or 9-1-1 for
emergencies.
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
Homework:
Due dates of assignments are specified in the
course calendar.
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.
Class Project:
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.
On March
12, the project proposal (up to 2 pages) is due. On April 28, the
final report (up to 10 pages) is due. For details about the project,
please read here.
Anaconda: Anaconda is the
leading open data science platform powered by Python.
Theano:
Theano is a Python library that lets you to define, optimize, and evaluate
mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
TensorFlow:
TensorFlow is an open source software library for numerical computation using
data flow graphs. Nodes in the graph represent mathematical operations, while
the graph edges represent the multidimensional data arrays (tensors)
communicated between them. The flexible architecture allows you to deploy
computation to one or more CPUs or GPUs in a desktop, server, or mobile device
with a single API.
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.
PyTorch: PyTorch is a deep learning
framework for fast, flexible experimentation.
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN:
Towards real-time object detection with region proposal networks." In Advances
in neural information processing systems, pp. 91-99. 2015. [pdf]
Dai, Jifeng, Yi Li, Kaiming He, and Jian Sun. "R-FCN: Object detection via
region-based fully convolutional networks." In Advances in neural information
processing systems, pp. 379-387. 2016. [pdf]
[source code]
Huang, Jonathan, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara,
Alireza Fathi, Ian Fischer et al. "Speed/accuracy trade-offs for modern
convolutional object detectors." arXiv preprint arXiv:1611.10012 (2016). [pdf]
(E.g., for Inception V3, extract features from the “Mixed 6e” layer whose
stride size is 16 pixels. Feature maps are cropped and resized to 17x17.)
Graves, Alan, Navdeep Jaitly, and Abdel-rahman Mohamed. "Hybrid speech
recognition with deep bidirectional LSTM." IEEE Workshop on Automatic Speech
Recognition and Understanding (ASRU), 2013. [pdf]
Deep reinforcement learning
UCL Course on reinforcement learning: [ppt]
[video]
References:
Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis
Antonoglou, Daan Wierstra, and Martin Riedmiller. "Playing
atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602
(2013).
Implementation
of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow.
Exercises and Solutions to accompany Sutton's Book and David Silver's course.
[link]
Generative Adversarial Network (GAN)
References:
Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley,
Sherjil Ozair, Aaron Courville, and Yoshua Bengio. "Generative
adversarial nets." In Advances in neural information processing systems,
pp. 2672-2680. 2014.
The General Language Understanding Evaluation (GLUE)
benchmark is a collection of resources for training, evaluating, and analyzing
natural language understanding systems.
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).
XnView:
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]
