Up-to-date information is at http://cse.unl.edu/~sscott/teach/Classes/cse478F10
| INSTRUCTOR: |
| Stephen Scott |
| 268 Avery |
| 472-6994 |
| sscott at cse |
| Office Hours: 3:00–4:00 Tues 3:30–4:30 Wed |
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 112
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.The Mitchell textbook is also available for rent at the bookstore.
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 may consult each other for assistance on the homework, but you must write up your results in your own words and indicate whom you consulted. 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 be assigned a topic related to the course material. You will prepare a detailed written (wiki-based) presentation of this topic. You will also make substantive edits of three other students' wikis. More details and topic assignments will come later. The wikis will be accessed via the CSCE 478/878 (Fall 2010) main wiki page. You can see examples of wikis on different topics on the CSCE 970 Spring 2009 wiki page. Your wiki is due by the end of the fourteenth week (December 3) and your edits to others' wikis are due Wednesday of the fifteenth week (December 8). 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. If your project involves an implementation, then you may be asked to also give a brief demonstration. 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 6–10), and if necessary, during the fourteenth week (November 29–December 3). You will submit your written report to me no later than 11:59 p.m. on December 8. 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. 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.
The above items will be weighted as follows:
| hwks: 30% | proj. report: 30% | proj. presentation: 20% | your wiki: 10% | editing others' wikis: 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.
Academic dishonesty of any kind will be dealt with in a manner consistent with the CS&E Department's Policy on Academic Integrity. You are expected to know and abide by this policy.