CMSC 5724 Project Page

Team Coding

Each team can contain up to 5 members and will implement a designated algorithm either in C++, Java, or Python. The implementation must be from scratch, i.e., it can use only functions from a standard library, for example:

Use of any function outside the above libraries is not permitted unless prior approval has been obtained from the instructor. All source code is subject to plagiarism scrutiny. All kinds of dishonesty will be reported to the university for disciplinary actions.

Deploying a programming language other than the above requires an approval from the instructor.

Project List

Each team needs to work on two arbitrary projects from the following list. The list will still growing and will contain 5 or 6 projects eventually.

Decision Trees
Margin Perceptron

Additional projects will be released after their topics have been covered in the lectures.

Project #1: Decision Tree

Goal

Implement Hung's algorithm for decision tree classification

Dataset

We will use the Adult dataset whose description is available here. The training set (adult.data) and evaluation set (adult.test) can be downloaded here.

Preprocessing

Remove all the records containing '?' (i.e., missing values). Also, remove the attribute "native-country".

Deliverables

An executable program, which should output a decision tree to the disk when given an input training set.
A readme file detailing how to use the program.
Source code.
A document describing (i) the decision tree built from the Adult training set, and (ii) a report on using the tree to classify the records of the evaluation set. The report should indicate, for each record in the evaluation set, its attributes and whether it has been classified successfully.

Project #2: Margin Perceptron

Goal

Implement the margin perceptron algorithm.

Dataset

Your implementation should work on any dataset in the following format:

The first line contains three numbers n, d, and r, where n is the number of points, d is the dimensionality of the instance space, and r is the radius.
The i-th line (where i goes from 2 to n + 1) gives the (i - 1)-th point in the dataset as:
x₁,x₂,...,x_d,label
where the first d values are the coordinates of the point, and label = 1 or -1.

We have prepared three datasets for you:
2d-r16-n10000
4d-r24-n10000
8d-r12-n10000

Deliverables

An executable program.
A readme file detailing how to use the program.
Source code.
A report explaining the margin of your classifier for each of the three datasets.