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Probabilistic Graphical Models 10-708, Fall 2007 |
Course
Project
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Your class project is an opportunity for you to explore an interesting multivariate analysis problem of your choice in the context of a real-world data set. Projects can be done by you as an individual, or in teams of two to three students. Each project will also be assigned a 708 instructor as a project consultant/mentor. They will consult with you on your ideas, but the final responsibility to define and execute an interesting piece of work is yours. Your project will be worth 30% of your final class grade, and will have two final deliverables:
1. a writeup in the form of a NIPS paper (8 pages maximum in NIPS format, including references), due Dec 3, worth 60% of the project grade, and
2. a poster presenting your work for a special ML class poster session at the end of the semester, due Nov 30, worth 20% of the project grade.
In addition, you must turn in a midway progress report (5 pages maximum in NIPS format, including references) describing the results of your first experiments by Oct 31, worth 20% of the project grade. Note that, as with any conference, the page limits are strict! Papers over the limit will not be considered.
Project Proposal:
You must turn in a brief project proposal (1-page maximum) by Oct 10th.
You are encouraged to come up a topic directly related to your own current research project or research topics related to graphical models of your own interest that bears a non-trivial technical component (either theoretical or application-oriented), but the proposed work must be new and should not be copied from your previous published or unpublished work. For example, research on graphical models that you did this summer does not count as a class project.
You may use the list of available dataset provided bellow and pick a less adventurous project from the following list of potential project ideas. These data sets have been successfully used for machine learning in the past, and you can compare your results with those reported in the literature. Of course you can also choose to work on a new problem beyond our list used the provided dataset.
Project proposal format: Proposals should be one page maximum. Include the following information:
· Project title
· Project idea. This should be approximately two paragraphs.
· Software you will need to write.
· Papers to read. Include 1-3 relevant papers. You will probably want to read at least one of them before submitting your proposal
· Teammate(s): will you have teammate(s)? If so, whom? Maximum team size is three students.
· Oct 31 milestone: What will you complete by Oct 31? Experimental results of some kind are expected here.
Project suggestions:
· Ideally, you will want to pick a problem in a domain of your interest, e.g., natural language parsing, DNA sequence analysis, text information retrieval, network mining, reinforcement learning, sensor networks, etc., and formulate your problem using graphical models. You can then, for example, adapt and tailor standard inference/learning algorithms to your problem, and do a thorough performance analysis.
You
can also find some project ideas below.
Project A: Brain imaging data (fMRI)
This data set contains a time series of images of brain activation,
measured
using fMRI, with one image every 500 msec. During this time, human subjects performed
40 trials
of a sentence-picture comparison task (reading a sentence, observing a
picture,
and determining whether the sentence correctly described the picture).
Each of
the 40 trials lasts approximately 30 seconds. Each image contains
approximately
5,000 voxels (3D pixels), across a large
portion of
the brain. Data is available for 12 different human subjects.
Available software: Matlab
software for reading the data, manipulating and visualizing it, and for
training some types of classifiers (Gassian
Naive Bayes, SVM).
Project A: Bayes network
classifiers
for fMRI
Project idea: Gaussian Naļve Bayes
classifiers
and SVMs have been used with this data to
predict when
the subject was reading a sentence versus perceiving a picture. Both of
these
classify 8-second windows of data into these two classes, achieving
around 85%
classification accuracy [Mitchell et al, 2004]. This project will
explore going
beyond the Gaussian Naļve Bayes classifier
(which
assumes voxel activities are conditionally
independent), by training a Bayes network
in
particular a TAN tree [Friedman, et al., 1997]. Issues youll
need to confront include which features to include (5000 voxels
times 8 seconds of images is a lot of features) for classifier input,
whether
to train brain-specific or brain-independent classifiers, and a number
of
issues about efficient computation with this fairly large data set.
Papers to read: "Learning to Decode
Cognitive States from Brain Images,"
Mitchell et al., 2004, "Bayesian Network
Classifiers" Friedman
et al., 1997.
Project B: Image Segmentation Dataset
The goal is to segment images in a meaningful way.
http://www.cs.berkeley.edu
Project ideas:
Project B: Region-Based Segmentation
Most segmentation algorithms have focused on segmentation based on
edges or
based on discontinuity of color and texture. The ground-truth in
this
dataset, however, allows supervised learning algorithms to segment the
images
based on statistics calculated over regions. One way to do this
is to
"oversegment" the image into superpixels (Felzenszwalb
2004,
code available) and merge the superpixels
into larger
segments. Graphical models can be used to represent smoothness in
clusters, by adding appropriate potentials between neighboring pixels.
In this project, you can address, for example, learning of such
potentials, and inference in models with very large tree-width.
Papers to read: Some segmentation papers from
Project C: Twenty Newgroups text data
This data set contains 1000 text articles posted to each of 20
online newgroups, for a total of 20,000
articles. For
documentation and download, see this website.
This data is useful for a variety of text classification and/or
clustering
projects. The "label" of each article is which of the 20
newsgroups it belongs to. The newsgroups (labels) are
hierarchically
organized (e.g., "sports", "hockey").
Available software: The same website provides an implementation
of a
Naive Bayes classifier for this text
data. The
code is quite robust, and some documentation is available, but it is
difficult
code to modify.
Project ideas:
·
EM text classification in the case where you
have labels for some documents, but not for others (see McCallum
et al,
and come up with your own suggestions)
Project D: Sensor network data
A 54-node sensor network collected temperature, humidity, and light data, along with the voltage level of the batteries at each node. The data was collected every 30 seconds, starting around 1am on February 28th 2004.
http://www-2.cs.cmu.edu/~guestrin/Research/Data/
This is a real dataset, with lots of missing data, noise, and failed sensors giving outlier values, especially when battery levels are low.
Project ideas:
· Learn graphical models representing the correlations between measurements at different nodes
· Develop new distributed algorithms for solving a learning task on this data
Papers:
· http://www-2.cs.cmu.edu/~guestrin/Publications/IPSN2004/ipsn2004.pdf
· http://www-2.cs.cmu.edu/~guestrin/Publications/VLDB04/vldb04.pdf
· Efficient Structure Learning of Markov Networks using L1-Regularization
Project E: Character recognition (digits) data
Optical character recognition, and the simpler digit recognition task, has been the focus of much ML research. We have two datasets on this topic. The first tackles the more general OCR task, on a small vocabulary of words: (Note that the first letter of each word was removed, since these were capital letters that would make the task harder for you.)
http://ai.stanford.edu/~btaskar/ocr/
Project suggestion:
· Use an HMM to exploit correlations between neighboring letters in the general OCR case to improve accuracy. (Since ZIP codes don't have such constraints between neighboring digits, HMMs will probably not help in the digit case.)
Project F: Precipitation data
This dataset has includes 45 years of daily precipitation data from
the
Northwest of the
http://www.jisao.washington
Project ideas:
· Weather prediction: Learn a probabilistic model to predict rain levels
Project G: WebKB
This dataset contains webpages from 4 universities, labeled with whether they are professor, student, project, or other pages.
http://www-2.cs.cmu.edu/~webkb/
Project ideas:
· Assign labels to the documents using both content as well as link information. You could use a CRF like model where the hidden variables are the class labels of the web-pages and the observed variables are the words in each web-page. The undirected edges between the labels are given by the hyper-link structure with direction ignored.
Papers:
· http://www-2.cs.cmu.edu/~webkb/
·
http://www.cs.berkeley.edu/~taskar/pubs/rmn.ps
Project H: Electoral Campaign Contribution data
This dataset provided below is compiled from the Federal Election Commission
(http://www.fec.gov/finance/disclosure/ftpdet.shtml) and contains
information about federal electoral campaign contributions from
elections from 1980-2006. There are 3 types of entities: Donors,
Committees, and Candidates. Donors contribute money to committees,
and committees then give money to candidates. Donors are individuals,
like Harry Q. Bovik or Ben Roethlisberger. Committees are
organizations, and may be devoted to a single candidate or several
candidates. For instance, a committee might be CMU Students for Ron
Paul, or the Machine Learning Researchers for Political Action.
Candidates are registered candidates for any federal election: Senate,
House, or Presidential.
http://www.cs.cmu.edu/~mmcgloho/local/data/fec_data.html
The indices for all three entities list name and address data, with several additional fields. Donors also have a listed occupation. Committees have data pertaining to each committee's interest. The index for candidates also includes information on party and election status. Full lists of features may be found in the readme.Project ideas:
- Temporal Models such as HMMs or DBNs, modeling financial transactions over time.
- Relational Models, predicting links between donors/committees, and committees/candidates. One could also create entities/links for features (Donor Harry Bovik ResidesIn Zip15213).
- Learning causal relationships in the data.
Project I: Deduplication
The datasets provided below comprise of lists of records, and the goal is to identify, for any dataset, the set of records which refer to unique entities. This problem is known
by the varied names of Deduplication, Identity Uncertainty and Record Linkage.
http://www.cs.utexas.edu/users/ml/riddle/data.html
Project Ideas:
- One common approach is to cast the deduplication problem as a classification problem. Consider the set of record-pairs, and classify them as either "unique" or "not-unique".
Papers:
Project J: Email Annotation
The datasets provided below are sets of emails. The goal is to identify which parts of the email refer to a person name. This task is an example of the general problem area of Information Extraction.
http://www.cs.cmu.edu/~einat/datasets.html
Project Ideas:
- Model the task as a Sequential Labeling problem, where each
email is a sequence of tokens, and each token can have either a label
of "person-name" or "not-a-person-name".
Papers: http://www.cs.cmu.edu/~einat/email-2004.pdf