Your project will typically involve addressing a novel problem or addressing an existing problem in a novel way. Your goal should be to advance a state-of-the-art technique, or introduce a new task in learning with limited supervision along with benchmarking basic approaches, and potentially proposing an interesting model for the new task. Refer to the schedule to find topics of interest. But feel free to be creative and come up with your own! If you need help with ideas for your project please talk to the TAs or the instructor. While certainly not a requirement for the class, students should actively consider submitting a paper at the end of the course to a top-tier conference in Computer Vision,  Machine learning, or AI.

Projects typically fall under one of these categories:

• Novel problem / task / application.

• Application/survey - compare a bunch of algorithms on an application domain of interest. These make most sense if you are expecting some interesting trend / finding in the analysis.

• Formulation/Development - formulate a new model or algorithm for a new/old problem.

• Analysis - analyze an existing algorithm.

Project teams should have 3-4 students (depending on enrollment). No more than 15 teams in the class total.

You may combine this with another course project but must delineate the different parts.

Slides should be made as visual (with videos, images, animations) and clear as possible. Students should practice their talks ahead of time to make sure they are of appropriate length -- not shorter by more than a few minutes, and certainly not longer (we will set a timer that will go off). The talks should be well organized and polished. Take a look at some example presentations: Example1, Example2.

Initial and update presentations 

**Note: The presentation times below are estimates and are subject to vary based on course enrollment.

Initial presentation: Each team will present for 7 min followed by 3 min of discussion. In the first presentation, teams will present a project proposal organized as follows:

• Problem statement: Clearly state the goal of your project. Specify the input and desired output.

• Related work: Briefly describe existing related work (with citations) and what your project brings to the table that these other works do not. The most relevant papers may not necessarily be papers listed on the schedule, so be sure to also look beyond the list.

• Approach: Describe the technical approach you plan to employ. Clearly state the assumptions of your approach.

• Experiments and results: Describe the experimental setup you will follow, which datasets you will use, which existing code you will exploit, what you will implement yourself, and what you would define as a success for the project. If you plan on collecting your own data, describe what data collection protocol you will follow. Specify if you plan on experimentally analyzing different characteristics of your approach, or if you will compare to existing techniques. Provide a list of experiments you will perform. Describe what you expect the experiments to reveal, or what is uncertain about the potential outcomes. If you have any preliminary results, please summarize those as well.

• Timeline: Present a timeline of the planned tasks/goals. Clearly state what you plan to complete by the next presentation. Break this down into two sets -- tasks that you are sure you will have completed and tasks that are a bit of a long shot but you would like to complete. Please also use a similar breakdown during the update presentations. You will be expected to try hard to stick to this timeline.

Update presentations: In the following two presentations, you will update the class on your progress. You will remind the class of your problem statement, and provide a quick recap of the approach. Remind us of your timeline from your earlier presentation, and then describe your current results, any challenges or issues that you faced, and an updated timeline. Presentations will be <=7 min. long and will be followed by 2 min. of discussion.

Final presentation (15% of final grade. In class, Nov. 26, Dec 3)

Each team will explain their project in a 10 min. presentation with an organization similar to the project proposal presentation, except now describing the actual outcomes rather than plans. In addition, also describe any challenges you faced, any insights on future extensions of the project. 2 min. of QA will follow each presentation.

Project video (15% of final grade. Due Dec. 10, 11:59 pm ET)

Teams will prepare a 1 min. YouTube video summarizing the project. The video is a teaser to convey the main points, and gain the viewer's interest in wanting to know more. It should be understandable by anyone familiar with AI. Please email the YouTube link to the TAs and the instructor.

See: Example 1, Example 2 , Example 3 , Example 4 , Example 5 , Example 6 , Example 7 , Example 8 , Example 9 , Example 10 .