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Project Proposal

Due by 11pm Wednesday 2/14

Team Forming

The first step in starting your project is team formation. Teams should ideally have 3-4 members. Some of you may already have a team, others may be more interested in specific topics. Its easiest to communicate over Piazza if you are looking to form a team. We have both the "project_teams" folder and the "search for teammates" link active.

Once you have formed your team, please create a group in bCourses. Under "People" on the left menu, select "Groups" and then under the Settings wheel "View User Groups". At this point an "Add User Group" button should appear (make sure you are actually logged into bCourses and not viewing it anonymously). Then you can name the group and invite people into it. Note: bCourses (i.e. Canvas) does not treat student-formed groups as "official" groups for assignment submission. Once all the student groups are created, we will copy them as formal project groups.

Project Topic

You're free to do any project you wish that uses deep networks and relates to the material taught in the course. Here are some examples from 2016 that student teams agreed to archive:

https://bcourses.berkeley.edu/courses/1453965/pages/projects

If you want to implement a state-of-the-art model for some task, good sources are conferences like NIPS Links to an external site., ICLR Links to an external site. and ICML which is archived in the Journal of Machine Learning Research Links to an external site.. If you have interests in specific areas you can try conferences like CVPR, ICCV and ECCV (computer vision), ACL and NAACL (natural language), ICRA and IROS (robotics) and Interspeech and ICASSP (speech recognition). Here are some other resources on research papers and datasets:

Writeup

For the project proposal assignment, we would like you to produce a 2+ page Project Proposal. You can either submit pure text or a pdf file. This proposal should include these sections:

The Team

Give a list of team members and their backgrounds.

Problem Statement and Background

A high-level statement of the problem you intend to address, i.e. recognition of objects in images. Be specific - i.e. "recognition" in this case means outputting a label for the object and also a bounding box for its location.

Give background on the problem you are solving: why it is important, what is the start of the art - ideally for both neural and non-neural approaches to the problem. Include some references to papers that describe the method you plan to use, or to build on.

The Data Source You Intend to Use

Describe the data source(s) you will use. If you're not doing one of the recommended projects, make sure you have access to the data you want to use *in the quantity and quality you need*. Ideally you should get all the data in your hands before starting your project. Several teams in related courses have run into trouble because they couldnt obtain the volume or quality of data they needed. Many online data sources (e.g. Yelp) strongly throttle the data volume you can get through their API. Others (Twitter) have a higher limit but have use restrictions that you must respect. Certain types of meta-data (e.g. location, demographics) in social media sources are both sparse and unreliable.

Describe how you plan to obtain the data, or how you got it if you already have it. Remember the goal of the project is to explore neural models, not to do data wrangling so you should do any such prep work asap.

Description of the Tools You Plan to Use

Describe the tools you plan to use for modeling in your project. You are free to use any tool, but our ability to help you (or for you to get peer help) falls off for less-popular tools. The ones you should have most luck with are:

  • Tensorflow
  • Caffe
  • Theano
  • Torch

Evaluation

How will you evaluate your model? Even for simple prediction tasks, there are a variety of tools you can use, e.g. ROC and lit plots and AUC measures.

Submission

Use this link to submit your writeup:

https://bcourses.berkeley.edu/courses/1468734/assignments/7871436

Make sure it includes Project Title and Project Members prominently at the top of the page. Please make sure project member names match exactly with registered campus names - i.e. the names in the "People" page in bCourses.

For CS294-129

There are additional requirements for students in the graduate version of the class. You will be expected to design and implement a suitable baseline model to compare with your chosen model. This requirement doesnt affect this assignment, but will affect some later ones. In terms of your team, its best if your team is all enrolled in the same version of the course. If you really cant do this, then your (future) team assignment submissions will have to include the extra elements (baseline model and performance comparison) in the final report, presentation and poster. Grading of these elements will be separate, and will be allocated only to the students taking the graduate version of the course.