Up-to-date information is at http://cse.unl.edu/~sscott/teach/Classes/cse478F12
| INSTRUCTOR: | TA: |
| Stephen Scott | Yuji Mo |
| 268 Avery | 206 Schorr |
| 472-6994 | |
| sscott at cse | ymo at cse |
| Office Hours: R 1:30–3:30pm and by appointment in Avery 268 |
Office Hours: M 2:30–4:30pm in Avery 13 |
PREREQUISITES: CSCE 310 (Data Structures & Algorithms) required (or similar experience), STAT 380/880 (Prob. and Stats.) helpful
TIME: 12:30–1:20pm MWF
CLASSROOM: Avery 110
TEXTBOOKS:
Required: Machine Learning, by Tom Mitchell. McGraw-Hill, 1997, ISBN 0070428077. [Book's web page includes errata.]
Useful for learning about support vector machines, but not required: An Introduction to Support Vector Machines, by Nello Cristianini and John Shawe-Taylor. Cambridge University Press, 2000. ISBN 0-521-78019-5.
Useful for learning about boosting, but not required: Boosting: Foundations and Algorithms, by Robert Schapire and Yoav Freund. MIT Press, 2012. ISBN 0-262-01718-0.
In this course you will learn several of the fundamentals and current trends in machine learning. Many of the approaches we will cover are applicable to several areas, including game playing, bioinformatics, text categorization, speech recognition, and machine vision. The emphasized topic in this course will be learning different types of classifiers, including decision trees, neural networks, support vector machines, Bayesian classifiers, and instance-based classifiers. We will examine these classifiers' strengths and weaknesses, and will study methods to theoretically and empirically evaluate them. Time permitting, we will also investigate reinforcement learning, which is a useful approach to automated system control, as well as review genetic algorithms and learning sets of rules.
There will be 2–4 homework assignments, each 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 the
handin program
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 non-computer-based problems (theoretical exercises) and
computer-based problems (implementations). The former is to help
you comprehend the theory and the latter is to help you understand how
the theory is used in practice. Late homework submissions will be penalized
exponentially: if your submission is m minutes late,
your final score will be multiplied by
You must use some document processing package (e.g. LaTeX) to write your homework submissions, and you must submit your reports electronically in pdf format (see "Help on Creating pdf Files" and "Help with LaTeX, etc."). 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 I cannot understand what you wrote due to poor writing, etc., then I cannot award full credit, even if your answers are correct. Thus I 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 native language, then I 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.
Another part of your homework grade will come from "mini-homeworks", in which you'll choose a simple concept to learn (e.g., "foods I like"), identify some positive and negative examples of your concept, and then incrementally build a portfolio of (manually crafted or machine-generated) hypotheses that approximate your concept. This portfolio will consist of a decision tree, a simple neural network, a Bayesian classifier, and an ensemble of decision stumps. You will briefly desribe your concept, your data, and your hypotheses in the course wiki.
This semester you will contribute to a collection of wikis on machine learning-related topics. You will either (1) make substantive contributions to the depth and/or breadth of five existing wikis, or (2) create and develop a wiki on a new, relevant topic. The wikis will be accessed via the CSCE 478/878 main wiki page. Your contributions are due by Wednesday of the fifteenth week (December 5). Of course, you are encouraged to complete them much earlier.
There will be no exams in this course.
In this course you will do a substantial project. This project can be: (1) a very extensive literature search and summary on a particular topic, (2) a good implementation and evaluation of a known result in machine learning, or (3) a small (but nontrivial) amount of original research related to machine learning. You may work on these projects individually or in small groups, though if you work in a group, my expectations will be much higher when I grade your project.
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 (December 3–7), and if necessary, during the fourteenth week (November 26–30). You will submit your written report to me no later than 11:59 p.m. on December 5. 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 September to early October) I will set a deadline for submission of 1–3 paragraph proposals on your projects. You must do this in order to get full credit for your project, and you must get my approval on it before starting work on your project. I 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 and be distinct from your wiki topic. 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.
The above items will be weighted as follows:
| hwks: 30% | proj. report: 30% | proj. presentation: 30% | wiki: 10% |
In computing your letter grade I 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. I will scale up from this base scale if I deem it appropriate. 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. Note that for students registered for CSCE 878, a B is required to pass the course; a B– is insufficient.
In general, students registered for CSCE 878 will be graded more stringently on everything and will have more problems to solve on the homework assignments.
All submitted work must be your own. No direct collaboration with fellow students, past or current, is allowed unless otherwise stated. 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.
Students with disabilities are encouraged to contact the TA or me 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.
GTA office hours are held in the CSE Student Resource Center in Avery 13A.
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.