Project Breakdown #

The project is worth 30% of your grade and will focus on applying machine learning algorithms and methods to real-world datasets to create meaningful insights and useful predictions. You will create a website with GitHub Pages for your project which will be used to publish three deliverables: a proposal (5%), a midterm checkpoint (10%), and a final report (15%).

CS 4641 students are required to use supervised learning. It is highly encouraged to use unsupervised learning methods as well.
CS 7641 students are required to use both unsupervised and supervised learning.

Team Composition #

Each project must be completed in a team of 5. Students from CS 4641 can only team up with students in CS 4641, and students from CS 7641 can only team up with students in CS 7641.

You will be forming your team on your own. Please use EdStem to find team members. If you cannot find a team, you will be randomly assigned to a team. Note that if you form a team with less than 5 people, we may randomly assign extra teammates.

Once teams have been formed, you will be assigned a mentor. It is highly recommended to set up meetings with your mentor.

Peer Evaluation #

After the proposal, midterm checkpoint, and final report, students will be required to complete a peer feedback form on CATME for every teammates. Failure to complete this feedback will result in a grade reduction.

This measure intends to identify non-contributing members. The teaching team reserves the right to reduce project grades down to a 0 in extreme situations if sufficient evidence is gathered.

General Project Guidance #

Please see the proposal, midterm checkpoint, and final report pages for specific guidance and requirements for each project deliverable.

Criteria #

Your project will be graded based on the following general criteria:

Was the motivation clear?

What is the problem?
Why is it important and why we should care?

Were the dataset and approach used effectively?

How did you get your dataset?
What are its characteristics (e.g. number of features, # of records, temporal or not, etc.)?
Why do you think your approach can effectively solve your problem?
What is new in your approach?
Effective visualizations? Are they relevant? Do they help you better understand the project’s approaches and ideas?

Were the experiments, results, and conclusion satisfactory?

What are the results?
How did you evaluate your approach?
Why do you think your results are satifactory? If not, what are the limitations? What is work that could be done to improve results?
How do you compare your method to other methods?
Effective visualizations? Are they relevant? Do they help you better understand the project’s approaches and ideas?

How was effective was the presentation?

Finished on time?
Effective visualizations? Are they relevant? Do they help you better understand the project’s approaches and ideas?
Is the use of text concise? Does it describe technical content succinctly? Does the amount of text overwhelm the audience?

Helpful Resources #

Sample Projects: In Canvas, there will be a pinned post under the Discussion tab named “Example Project from the previous semesters”.
Project Ideas: When brainstorming ideas for a project, reference the Stanford Project Examples.
Dataset: When sourcing datasets, reference the Dataset Ideas.
Seminars: We will have project seminars where TAs will present their ML projects, prior students’ projects, and/or research and industrial projects. Students will also have the opportunity to ask questions about their project. Seminars will be streamed online and recorded. Posts on EdStem will be made for each seminar with details on when and how to join each meeting along with recordings.
Compute: Google Colaboratory allows free access to run Jupyter Notebooks using GPU resources. The Google Cloud Platform and AWS Educate are also good resources. The GitHub Student Developer Pack also offers free Microsoft Azure and Digital Ocean credits. This semester, we are also offering PACE ICE, Georgia Tech’s in-home cluster to students.