






By completing this project, students will gain an understanding of the capabilities of alternative machine learning algorithms and gain experience in using these algorithms. The project fosters discussion and analysis of machine learning topics including the ones listed below:
 Appropriate representations of input information.
 The advantages and disadvantages of alternative concept representations.
 The performance of learning algorithms as a function of concept complexity and number of training examples.
 Formalizing binary and multivalued learning problems.
 Designing mechanisms for improving the learning power of existing algorithms.













Students should have basic knowledge of algebra, discrete mathematics, logic, and statistics. Another prerequisite is the data structures course. While not necessary, experience with Java would be of help as the basic tool needed for this project  the Weka Machine Learning system  is implemented in Java. Before starting the project, students may want to cover the recommended reading so that they understand better the fundamental concepts of Concept Representation and Machine Learning. In support of the exercises and project, students should download Weka which is available at http://www.cs.waikato.ac.nz/~ml/weka/index.html.













For an introduction to machine learning and details about decision tree algorithms, students can read chapter 18 in the following book; naive Bayes details are provided in chapter 13 of this book; neural network details are in chapter 20 of this book:
More details on these learning algorithms as well as instancebased learning algorithms can be found in Tom Mitchel's Machine Learning textbook.













The detailed project description is available in the TXT file description.txt.


This project is customizable to accommodate different approaches to teaching and different implementations. Additional exercises are also included for students seeking more extended challenges.














A sample syllabus is not available.
Additional readings are included in the Background section above.






