One of the main goals of this course is to provide students with the opportunity to apply machine learning techniques to real-world problems. To this end, you will be required to complete a final project that demonstrates your understanding of the course material and your ability to apply it to a specific problem or question that you’re interested in pursuing.

While you can choose any topic for this course, try to select a project where you can combine existing analytical methods with machine learning techniques; or where you can study the problem at hand in a scientific manner. That is, you should aim to apply the scientific method to your project, which includes formulating a hypothesis, collecting and analyzing data, and drawing conclusions based on the evidence. Once you get the accuracy of your model, you should be able to interpret the results and make recommendations based on your findings.

The following will provide you with a detailed overview of the requirements.

1. Motivation and Question

• Description: Clearly define the problem you intend to solve or the question you want to answer. Explain why this problem/question is significant and relevant. Typically, this section should include a brief introduction to the problem, its context, and its importance. A brief literature review (containing links to similar projects or papers) is also recommended. Don’t be afraid to be creative and think outside the box!
• Goal: Ideally, you should state the specific goal of your project, such as building a model to predict a certain outcome, identifying patterns in a dataset, or solving a specific problem. The goal should be clearly defined and measurable, and it should be directly related to the problem or question you’ve identified. If you don’t have a clear idea of the goal at this stage, don’t worry; you can refine it as you go along.

2. Data Description

• Sources: Detail where and how you will collect your data. Include sources, methods of data collection, and any challenges you anticipate. It is perfectly acceptable to generate your own data if you plan to develop novel algorithms.
• Data Type: Describe the type of data (e.g., images, text, numerical) and its relevance to your problem.

3. Process and Timeline

Break down the project into key steps (e.g., data collection, preprocessing, model building). These steps will give you an idea on how long your project will take and what resources you will need. I recommend starting as soon as possible!

4. Methodology

• Approach: Describe the models, methods, or algorithms you plan to use (e.g., deep learning, regression analysis).
• Justification: Explain why these methods are suitable for your problem.

5. Evaluation Strategy

• Metrics: Define how you will evaluate the performance of your model (e.g., accuracy, precision, recall).
• Validation: Explain how you will validate your results (e.g., cross-validation, test data).

Submission Guidelines:

• Length: Proposals should be 1-2 pages max.
• Format: Please submit a PDF document to Moodle.
• Due Date: The proposal is due on Friday February 16.

Encourage students to be creative and think critically about their chosen topic. This project is an opportunity to explore an area of personal or professional interest in the field of machine learning. If you have questions or need help, please don’t hesitate to ask!