Your overall course grade will be comprised of the following components, and their weights:

Assignment	Release Date	Submission Date	% of Final Mark
Workshops	Monday (Weekly)	Wednesday (Following Week)	10%
Project 1	Monday (Week 5)	Friday (Week 7)	20%
Project 2	Monday (Week 10)	Friday (Week 12)	20%
Exam	TBA	(Same Day)	50%

Workshops (10%)

Students are required to to turn in completed worksheets via GitHub (see Course Components) by Wednesday 5pm UK time before the next week’s workshop. Marking will be binary, with any reasonable attempt to complete the worksheet receiving full credit. No extensions will be granted for submitting these worksheets. Solutions will be provided after the deadline.

Projects (40%)

Either individually or as a team of up to 4 students, you will be responsible for the completion of two projects for this course. The goal of these projects is to develop understandable and validated models using the tools and techniques covered in this class. Along with reproducible code, you will be required to submit a report to communicate your research.

Project 1 (20%)

Specific details on this project will be made available on Monday (Week 5).

Project 2 (20%)

Specific details on this project will be made available on Monday (Week 10).

Required Structure & Formatting

We will provide a template Jupyter notebook in each teams (or individuals) project repository. Each template will include the required sections along with brief instructions on what should be included. Your completed assignment must follow this structure - you should not add or remove any of these sections, if you feel it is necessary you may add addition subsections within each. Please remove the instructions for each section in the final document.

All of your work must be contained in the notebook, we will only mark what is included in this file.

Our expectation is that most projects will be roughly 10-15 pages in length (text & figures, excluding code). Your notebook must include all of your work, but make sure that you are only retaining required components, e.g. remove unused code and figures (if a figure is not explicitly discussed in the text it should not be in the final document).

Overall, your project will be partially assessed on your organization/presentation of the document - it should be as polished and streamlined as possible. We highly recommend that you check the appearance of your rendered PDF before submitting, as its appearance can differ significantly from the notebook.

Submission

Project 1 is due Friday (Week 7) by 5 pm (UK local time) and project 2 is due Friday (Week 12) by 5 pm (UK local time). You are expected to submit your completed work as follows:

• Submit a PDF of your report (generated from a Jupyter notebook) on the project GitHub repository.
• Submit your ipynb notebook file and any supplementary data and script files on the project GitHub repository.

Both submission steps are necessary for your work to be considered submitted. Standard late penalties will apply if either piece is not submitted by the deadline.

Late submissions will be accepted up to seven calendar days of the deadline, with a late submission penalty applied of 5% of the overall mark for each 24 hours past the deadline. Full details on the extensions and late submissions policy.

Marking Rubric

The project will be marked out of 100, and we will be using the following rubric to roughly guide the marking:

>90: The code runs without errors. Models are implemented, fit, and assessed correctly. The final model achieves a high level of predictive accuracy and is well documented and described in the writeup. There is significant and creative additional investigation of the problem including the use of addition data sources for features. Potentially could be used as a model answer. Write-up evidences deep understanding of the data and the model(s).

80-89: The code runs without errors. Models are implemented, fit, and assessed correctly. The write up is generally good and the code is appropriately commented. The final model achieves a reasonable level of predictive accuracy and is well documented and described in the writeup. There is moderate additional investigation of the problem. Write-up evidences good understanding of the data and the model(s).

70-79: The code runs without errors. Models are implemented, fit, and assessed correctly with only minor issues. The write-up is reasonable but could be better. Write-up evidences adequate understanding of the data and the model(s).

60-69: The code runs without errors. Models are implemented, fit, and assessed correctly with only moderate issues. The write-up is ok but could be better, includes some moderate errors or omissions. Write-up evidences adequate understanding of the data and the model(s).

50-59: The code runs with some errors. Models are implemented, fit, and assessed but with some significant issues in implementation and or understanding. The write-up is marginal and includes some significant errors or omissions. Write-up evidences an incomplete understanding of the data and the model(s).

<49: Significant issues with the code, model(s), and/or the write up. Write-up evidences an incomplete understanding of the data and the model(s).

Using these criteria, specific rubrics will be used to assess each of the 4 required sections of the project.

Final Exam (50%)

[Insert Information]

3 questions on ML methodology; 1 questions on understanding of Python code/output.

A sample exam will be provided later in the course. A version with solutions will be posted after [DATE].

Any questions about the sample exam or the solutions should be posted on Piazza.