The term project reports are due on April 25, 2019 at 1:00pm. A project report should be organized like a conference paper with abstract, introduction, related work, methodology, experimental results, discussion of results, and conclusion sections.

Term project formatting The term project report format is flexible, you can use either a single column or double column format. Also there is no target page length. The completeness of the report would be judged by its content.

The term project reports should be self-explanatory. This means that the key parts of the text, description of the methods and their justification should be included in the report. In other words, the report should be written is a way the content is possible to understand without going to and reading other works/literature the report refers to. For example, a sentence like: "we implemented and tested WonderML method [reference]" without further explanation and a brief description of the method are not sufficient and (not !!!) self-explanatory.

References Please do not forget to give the credit whenever appropriate. Give relevant references to past work on methods or works that use the same dataset. If your implementation relied on the existing code please do not forget to acknowledge the code source.

Code The code you have developed (not the data !!!) should be submitted in the zip file via a separate link Term project submission link. Please acknowledge any code/packages used to develop the models or the ML algorithms.

The term projects are group projects with two students forming the group. Randomly selected groups are listed in a separate document posted on the course web page. In the case the students encounter any problems forming the group, they may still choose to work alone, and indicate this in the email (sent to the instructor by 5:00pm March, 19, 2019) and in the proposal.

Possible conflicts
In general, the project selected for CS 2750 should not be the same or overlap significantly with projects used to satisfy requirements of other courses (e.g. vision, image analysis, pattern recognition or NLP courses) Please note that any possible conflict should be discussed and approved by the instructors of both courses prior to the project.

Here is a link to the Intro to ML in python document that provides a guide to building ML solutions with the help python libraries. Please note this document is under development and is likely to be updated and refined periodically.