Project Guideline
The course project is one of those great opportunities in which you can apply your newly learned skills towards real-life problems. It offers you the chance to build a Deep Learning pipeline, tune and debug a machine learning model, and experiment with one of the world’s most successful Computer Science approaches. It is also responsible for a significant portion of your final grade.
We strongly suggest you do the project in a team. A team can have a maximum of two members and remember we will review each member’s contribution individually. For the project topic, there are two options, you can either define the project based on our default topics or implement your own idea. It is OK to use your thesis as the course project but keep in mind that you still need to match up our requirements such as writing a final report, submitting the project proposal, and reporting the progress on the corresponding milestone.
Additionally, we have to make sure your idea is feasible and also in the scope of the course. Thus before starting to work on your project, the proposal should be approved by us.
This document will guide you through milestones as well as their evaluation policy. Please read them carefully and reach us if you have any question.
Proposal
No matter how you came with the project idea (whether it’s towards your Master/PhD thesis or one of the pre-defined topics), we need you to complete the proposal. It shows how well you have understood the problem and how well you have explored the problem space. We’ll review the proposal and inform you whether we think the scope of the project is too narrow or too wide.
- Title: A statement to briefly explain the problem/task and a solution you’re going to propose. Example: “Grammatical error corrections using an augmented sequence to sequence model”.
- Problem definition: Describe what problem you are going to solve. What goal(s) you’re trying to achieve. Why do you think it is important/challenging? Explain the inputs and outputs of your system in detail. Give us an example if applicable.
- Dataset: A high-quality dataset is a critical prerequisite for deep learning. Please specify the dataset you will use, also include its size and license (if you want to evaluate your model on several datasets, go ahead and list all of them). If you need any preprocessing step, please describe it. Sometimes it is necessary to collect data yourself. Explain how you are going to do that and how you plan to label the collected data (if applicable).
- Evaluation metric: This is a measurement of how well your model performed its desired task. It should be a well-defined, numerical, comparable, automatic evaluation metric (for example: F1-measure or BLEU score).
- Baseline method: Describe a baseline method for solving this problem. Make it clear that you will implement it yourself, or will use a previously published score. Baseline is a trivial approach. The first idea that can be thought. You suggest an algorithm that should be better than the baseline at least. For example feeding a word with its neighbors is a baseline to name entity recognition task.
Your submission is first examined in terms of an accurate description of the required fields. It will also be judged on creativity in defining the problem and the quality of your write-up.
Progress Report
Up until this point, you should have implemented a preliminary and simple version of your idea, this implementation doesn’t have to be fully optimized at this point, and it doesn’t have to contain all the features you want yet. You must report these items:
- Proposed algorithm: Describe your proposed algorithm for solving the problem in detail, use a concrete example to show how your model and algorithm work. Your description must be precise and specific (what are your model’s inputs, outputs, variables, etc.).
- Results: Report the experimental results of using this basic version of implementation and compare the results with the baseline methods. Descriptions should be quantitative (using tables, metrics, plots, etc.). Explain whether you were expecting these results or not.
- Improvement plan: Explain your plan for improving the results in the future (e.g. you can look for the flaws in the proposed method, try to find a way to fix them, or you can simply search for a more advanced model than what you already have). Note that you must implement these suggestions and put them to use for the final version.