CSE 6363 - Machine Learning
Class Presentation and Project
Presentation Topic Due: November 8, 2001 (midnight)
Project Proposal Due: November 8, 2001 (midnight)
Project Due: December 11, 2001 (midnight)
On or before the above due date you should email me (email@example.com)
the title of the paper(s) for your class presentation. Also, include date
preferences for your presentation (11/15, 11/20, 11/27 or 11/29).
Presentations will last approximately 30 minutes. Since you will be graded
on (among other things) your coverage of the material in the paper, I
strongly recommend you use PowerPoint, or some other electronic form, for
your presentation to expedite the delivery of information. I would suggest
a more recent paper of interest to you from conference proceedings of the
International Conference on Machine Learning (ICML), National Conference on
Artificial Intelligence (AAAI), or International Joint Conference on
Artificial Intelligence (IJCAI). The Machine Learning Journal or Journal
of Machine Learning Research are also good sources for papers. The topic
of your paper presentation may coincide with your project topic. Paper
selections must be approved by me.
On or before the above proposal due date, you should email me
(firstname.lastname@example.org) a proposal for your machine learning class project.
The project proposal should describe the problem or issue you are
addressing, the relevance of the problem to machine learning, your approach
or approaches to the problem, possible empirical and/or theoretical
analyses of the approaches, and the expected results. Below is a list of
some possible project ideas. The project is a significant part of this
course, and I expect you to spend a significant amount of time preparing
your final report. Therefore, I encourage you to turn in your project
proposal as soon as possible and get started early. Project proposals must
be approved by me.
The project will consist of a writeup describing, in more detail, the
problem, relevance, approaches, analyses, results, and conclusions about
the advantages and disadvantages of your approach. If your project
involves some programming, turn in copies of all code. If your code is a
modification of existing code, be sure to clearly indicate your
modifications. Also turn in any data used and program output traces for
- Applied Learning. Some of the biggest success stories of
machine learning come from the application of one or a small number of
learning methods to a specific problem. A project on applied learning
would first identify the application domain, collect data, run appropriate
learning methods, and analyze results in terms of relevance to the domain
(ideally done by a domain expert). Typically, this approach goes through
several iterations of modifications and tweaks to the data representation
and learning method in order to squeeze as much performance as possible
from the method. The project would report the data representation,
modifications made at each iteration, the intermediate and final results,
and an evaluation of these results and the method in terms of applicability
to the domain.
- Improvements to Learning Methods. Several of the sections on
machine learning approaches suggest problems with the approach and possible
solutions (e.g., noise and missing values in inductive learning, incomplete
and/or incorrect domain theories in deductive learning, or combining
multiple learning methods to overcome weaknesses in the isolated methods).
The only way to know if these improvements work is to implement them and
test them empirically in a variety of learning tasks. A project for
evaluating improvements to a learning method would implement several
possible approaches to the improvement and test each to determine the
effectiveness of the improvement.
- Empirical Comparison of Machine Learning Methods. In the
homework you compared the performance of several inductive learners. This
comparison can be expanded into a project by increasing the number of
learners and/or data sets (both real and artificial). The learners do not
have to be limited to inductive techniques. The project would involve
running several experiments on different combinations of learners and data
sets, comparing results, and determining the underlying reasons behind
discrepancies in the performance of different learning methods.
- Survey of Machine Learning Methods. This course has introduced
only a subset of the available machine learning methods. Many other
methods exist, and new methods are constantly being developed. A survey of
methods for a particular type of learning would involve a literature search
for old and new methods, descriptions of these methods, and generalizations
across several methods indicating patterns in the approaches to learning.
Implementations and empirical comparisons of selected methods would
increase the validity of your conclusions regarding advantages and
disadvantages of different approaches.