You are expected to work in Teams of 2 ; teams of 1 or 3 are acceptable, if the project is suitable and agreed by instructor.
Expected load is about same as a HW (25 hours) If larger in scope, you can get a reduction from the regular HW load.
Proposal Due : June 24, about 1-page bullet-format. Make sure to describe data and the task, what was/is being done about it, what is that you plan to do
Draft/code Due : July 29
Final report Due : August 12
Your project for the course should be one of the following :
This is common choice, to solve a classification/prediction problem. Can use a class dataset This kind of project requires the student(s) to look into an advance topic/algorithm; several initial readings might be necessary. Here and here are some collections of past student projects from Stanford University.
Empirical studies of what works and what does not, in terms of modifying or combining learning algorithms. While no math proofs are required, a hypothesis has to be formulated and shown to be consistent over multiple datasets.
Theoretical work includes generalization error bounds, proves of convergence, running time analysis, etc. The work has to be somehow novel, and the concepts non-trivial. This choice is the hardest
You can work on your existing project, especially (but not necessary) if it is work advised by a faculty at NEU. The work for the ML class has to be done during the class term, has to be appropriate for the material covered in class, and has to be yours even if the project is joint work with others.
Your report has to be in pdf format, not longer than 10 pages. Make sure you state the problem you solve, why is it important and give an outline of how you solve it.You should include reports of the experiments you run, analyses and profs.
The code should not be printed (unless your project is about a smart implementation; even then, only include the relevant segment of the code).