Syllabus for CSCE 496/896-005 (Spring 2018)

Up-to-date information is at


Stephen Scott Paul Quint
sscott at cse pquint at cse
Office Hours:
T 1:00–2:00
R 2:30–3:30
and by appointment
in Avery 268
Office Hours:
MW 3:30–4:30
in Avery 12

PREREQUISITES: CSCE 310 or 311 (data structures and algorithms) required (or similar experience), STAT 380/880 (probability and statistics) helpful. Machine learning background is not necessary, but you must be a competent programmer.

TIME and ROOM: 10:30–11:20 Mon/Wed/Fri and 4:30–5:20 Mon, in Avery 110



Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron, O'Reilly Media, 2017, ISBN: 9781491962299.

Useful for general introductions to machine learning, but not required:


In this course you will learn several of the fundamentals and current trends in deep learning. Many of the approaches we will cover are applicable to several areas, including game playing, bioinformatics, text categorization, speech recognition, autonomous systems, and machine vision. The emphasized topics in this course will be different approaches and technologies in deep learning, including various activation functions and regularizers, as well as convolutional layers, recurrent networks, and autoencoders.



Every class meeting on Monday afternoon will be a hack-a-thon. Each hack-a-thon session will put into practice the concepts covered in lecture. In each session we will guide you through downloading and setting up data sets and network architectures, as well as tuning hyperparameters and evaluating the results. You will indiviually submit the results of each week's hack-a-thon assignment via handin by the following Thursday at 11:59pm. Late submissions will not be accepted.

Hack-a-thons will be interactive sessions, in which you are expected to bring with you a fully charged laptop, as well as a smartphone or some other means to log into HCC via two-factor authentication.

Prior to the first hack-a-thon session on January 8, you need to join the HCC class group and set up Duo two-factor authentication. If you do not already have an HCC account, go to to request one, in the class group cse496dl. If you already have an account, send an email to to ask to join the class group cse496dl.

If you have not already done so, you should also install on your smartphone the Duo Mobile app to use two-factor authentication to log into HCC. You will also need to go in person to the HCC office in Schorr 118 to set up the authentication system. More information on this is at

All sessions will use the TensorFlow library in Python. If you do not already know Python, you are expected to learn it on your own. We will not teach basic Python in this course.


There will be 4–5 homework assignments, which you will work on in small teams. 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 implementation and testing of various neural network architectures. Late homework submissions will be penalized exponentially: if your submission is m minutes late, your final score will be multiplied by 2m/60. Thus you will lose 16% of your points if you submit 15 minutes late, 29.3% of your points if you submit 30 minutes late, 50% if you submit 60 minutes late, etc. (amount of lateness is measured by the time stamp given by cse's handin). Thus unless you are making very significant improvements to your submission, it is better to submit a partially completed homework assignment than a late one. If you have a valid excuse for a late submission (e.g., illness), contact us as soon as possible.

You and your teammates 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 is as important as the results themselves, and 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!

Note that to use handin, you need a CSE login account. To get a CSE login account (if you are not a CSE student), go to

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.


There will be no exams in this course.


In this course you and your team will do a substantial project, in which you will characterize a significant problem amenable to a deep learning solution, study the related work to this problem, develop one or more deep learning approaches to this problem, and evaluate your approaches.

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 fifteenth week (April 23–27), and if necessary, during the fourteenth week (April 16–20). You will submit your written report no later than 11:59 p.m. on April 25. 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.

Later this semester (late February to early March) we will set a deadline for submission of 1–3 paragraph proposals on your projects. Also, in late March, you will submit to us a brief progress report and meet with us for a check-in on your project. You must do both of these in order to get full credit for your project, and you must get our approval on your proposal before starting work on your project.


The above items will be weighted as follows:

hack-a-thons: 20% hwks: 30% proj. report: 25% proj. presentation: 25%

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 we deem it appropriate. Thus, 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. Note that for students registered for CSCE 896, a B is required to pass the course; a B– is insufficient.

In general, students registered for CSCE 896 will be graded more stringently on everything.



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 or a failing grade for the course itself.

Attendance Policy

Attendance at all officially scheduled class meetings (class and lab sections) is expected. Students are responsible for knowing all material discussed in class meetings. Changes to class and lab schedules and assignments will be announced in class or lab.


Students with disabilities are encouraged to contact us for a confidential discussion of their individual needs for academic accommodation. This includes students with mental health disabilities like depression and anxiety. It is the policy of the University of Nebraska-Lincoln to provide 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, 232 Canfield Administration, 472-3787.


The CSE Department has an anonymous suggestion box at that you may use to voice your concerns about any problems in the course or department if you do not wish to be identified.


GTA office hours are held 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 22 February 2018; please report problems to sscott.