Up-to-date information is at http://cse.unl.edu/~sscott/teach/Classes/cse970S17/
| INSTRUCTOR: | INSTRUCTOR: |
| Stephen Scott | Vinodchandran N. Variyam |
| 268 Avery | 262 Avery |
| sscott AT cse | vinod AT cse |
| Office Hours: T 10:00–11:15am R 2:00–3:15 and by appointment in Avery 268 |
Office Hours: MW 2:00–3:15pm and by appointment in Avery 262 |
PREREQUISITES: CSCE 310 or CSCE 311; MATH 314/814; MATH/STAT 380 or STAT 880 or ELEC 305
TIME: 10:30–11:20 MWF
CLASSROOM: Avery 110
TEXTBOOKS:
In this course you will learn several of the fundamentals and current trends in pattern recognition, with an emphasis on deep learning techniques. These approaches are rigorously justified and have made amazing advances in many application areas such as image analysis. We will study the mathematical and algorithmic underpinnings of deep learning, as well as applications.
There will be a series of homework assignments, which you will work on in teams
of size two. Each homework is due by 11:59 p.m.
on its due date.
(cse's system clock, which timestamps your submissions,
is the official clock for this course. Do not assume that just because
handin
allows you to submit that the deadline has not passed; you are
responsible for ensuring that your files are submitted and timestamped before the
deadline.) These assignments will include
implementations of deep neural network architectures and empirical evaluation of
them.
Late homework submissions will be penalized
exponentially: if your submission is m minutes late,
your final score will be multiplied by
You and your teammate must use some document processing package (e.g., LaTeX) to write your homework submissions, and you must submit your reports electronically in pdf format. You must also write as clearly and concisely as possible. Presentation of your results will be heavily weighted in grading. If we cannot understand what you wrote due to poor writing, etc., then we cannot award full credit, even if your answers are correct. Thus we recommend that you ask someone to proofread your write-ups before you submit them, to check for clarity, typographical errors, etc. If English is not your first language, then we strongly recommend this!
Finally, ensure that all your files (e.g., program code, homework write-ups) are reasonably well-protected. You will be held responsible if someone copies your files and submits them as homework solutions.
In this course you and your homework teammate will do a substantial project on a topic that is related to deep learning. This project can be: (1) a good implementation and evaluation of a known result, in or (2) a small (but nontrivial) amount of original research.
You will summarize your project results in a written report and an oral presentation. The written report must use a professional writing style similar to that found in an ACM or IEEE journal, including abstract, introduction, summary of related work, your contribution, references, and an appendix (if necessary). The oral presentation will be to the entire class at the end of the semester, during the fourteenth week (April 17–21) and the fifteenth week (April 24–28). You will submit your written report to us no later than 11:59 p.m. on April 26 (the Wednesday before Finals Week). In accordance with UNL policies, you have now been informed in writing of the nature and scope of this project prior to the eighth week of classes.
By 11:59 p.m. on February 26, you and your partner will submit a 3–5 paragraph proposal on your project. You must do this in order to get full credit for your project, and you must get our approval on it before starting the work. We will provide a list of possible topics later this semester, but you may propose your own topic as well. To be a valid topic, it must go beyond the scope of the course. So your project could be on a topic we did not cover in class at all, or could more deeply explore a topic we covered in class.
| homeworks: 30% | project report and code: 35% | project presentation: 35% |
In computing your letter grade we will start with the following base scale, where s is your final score:
| s ≥ 90% ⇒ A | 80% ≤ s < 90% ⇒ B | 70% ≤ s < 80% ⇒ C | 60% ≤ s < 70% ⇒ D | s < 60% ⇒ F |
You will receive a "+" with your grade if the last digit of your score is ≥ 7, and a "–" if the last digit is < 3. We will scale up from this base scale if necessary. So if you get an 87% in this course you are guaranteed a B+ (similarly, an 82% guarantees a B–), but your grade might be higher depending on your performance relative to the rest of the class and your level of class participation.
All submitted work must be your own. No direct collaboration with fellow students, past or current, is allowed unless otherwise stated (e.g., your homework partner). The Computer Science & Engineering Department has an Academic Integrity Policy. All students enrolled in any computer science course are bound by this policy. You are expected to read, understand, and follow this policy. Violations will be dealt with on a case by case basis and may result in a failing assignment grade or a failing grade for the course itself.
Students with disabilities are encouraged to contact us for a confidential discussion of their individual needs for academic accommodation. It is the policy of the University of Nebraska-Lincoln to provide flexible and individualized accommodations to students with documented disabilities that may affect their ability to fully participate in course activities or to meet course requirements. To receive accommodation services, students must be registered with the Services for Students with Disabilities (SSD) office, 132 Canfield Administration, 472-3787 voice or TTY.
The CSE Department has an anonymous suggestion box at http://cse.unl.edu/department/suggestion.php that you may use to voice your concerns about any problems in the course or department if you do not wish to be identified.
Help with many CSE issues can be obtained in the CSE Student Resource Center in Avery 12.
It is CSE Department policy that all students in CSE courses are expected to regularly check their email so they do not miss important announcements.
Last modified 16 February 2017; please report problems to sscott AT cse.