1. 2014 ML Project2: Goal: Do some real machine learning; learn you to use machine learning to make sense out of data. Group Project—4 (3) students per group A project you are interested in works better Project2 is dataset driven; you try to find a dataset with an interesting task associated and apply different machine learning techniques to the dataset and compare and summarize the obtained results/developed algorithms. If you have an usual idea that does not fit the Project description of Project2  talk to Dr. Eick An alternative approach would be to develop an idea and to evaluate this idea in different contexts Usually involves additional reading Using software packages OK, particularly comparing algorithms is fine.

2. Requirements and Schedule Project proposals (due March 27 the latest!) –1 single-spaced page –Problem, data, and planned approach Progress report (due April 8) –two pages –Expected scope of the project and approach(es) investigated –Brief, self-contained description of progress 11-minute Presentation (April 21(2 groups) or April 23(7 groups)) Final report (due April 24/25) –Body (~9-11 pages of text, in ACM-SIGKKD format, single- spaced) with references and citations. Can have appendices. –Should have good, although short introduction/abstract (like a conference paper) and discuss related work

3. Example Project Themes Predicting senator’s votes based on campaign contributions Customer and product clustering for recommender systems Comparing Naïve Bayes and Neural nets for text classification Road extraction from Aerial images Predicting success of major league baseball teams Exploring methods for handwritten digit recognition Differences between ML and MAP estimators Using Multi-dimensional Scaling for Dataset Visualization A belief network tool predicting the presence and onset of a particular disease. Predicting the future success/failure of graduate students

4. More Comments Project2 As the project is a group project, use your project resources wisely; topics that can be partitioned more easily into subtasks are a better choice for Project 2 Websites of other machine learning courses are a valuable source to find interesting datasets with associated questions to investigate; however, you explore methods of you own to answer the questions at hand, rather than repeating that what was done in the past. http://www.cs.cmu.edu/~epxing/Class/10701/project.htm http://www.cs.cmu.edu/~epxing/Class/10701/project.htm Reference all sources you used in your final report including your own publications (  not doing so is cheating!!!); conduct a web-search to find additional related work Evaluating which the techniques you developed/used work for the problem at hand is very critical for the success of the project; moreover, make an overall evaluation how well the problem at hand was actually solved. Just using a piece of machine learning software might not be a good idea; comparing different approaches, on the other hand, is okay. There has to be some learning in your project!! Make a schedule for the project with milestones and deadlines!.