Class Presentation and Project
Presentation Topic Due: April 8, 2004 (midnight)
Project Proposal Due: April 8, 2004 (midnight)
Project Due: May 11, 2004 (midnight)
On or before the above due date you should email me (firstname.lastname@example.org) the title of the
paper(s) for your class presentation. Also, include date preferences for
your presentation (see Schedule for possible
presentation dates). Presentations should be approximately 25 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), International Conference on Knowledge Discovery and Data Mining
(KDD), 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 (email@example.com) 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 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 achieve the highest performance possible.
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 learning algorithms. 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.