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Update to grade weightings
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@trgardos
trgardos committed Sep 2, 2024
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Expand Up @@ -242,18 +242,20 @@ Your final grade will be a weighted sum of grades received in the following cate

| % of Grade | Category | Notes |
| ---------- | -------- | ----- |
| 15% | Assignments | Grading rubrics are available on individual assignment pages. You must pass the diagnostic assessment to receive a passing grade in the class. |
| 15% | Attendance and Participation | Students are graded on their in person attendance, class participation and individual contributions towards the project. |
| 50% | Project | Students are graded on their individual contributions and overall quality of the project (see below) |
| 5% | Peer evaluations | Each student will receive midterm and final peer evaluations from members of their project |
| 5% | Midterm presentation | Student teams will present their projects to the rest of the class. All students on a team will receive the same grade. |
| 10% | Final presentations | Student teams will present their projects to the rest of the class. All students on a team will receive the same grade, barring special circumstances. |
| 15% | Assignments | Grading rubrics are available on individual assignment pages. |
| 10% | Attendance and Participation | Students are graded on their in attendance, participation and engagement during lectures. |
| 15% | Presentations | Student teams will give interim and final presentation on their projects to the rest of the class. All students on a team will receive the same grade. |
| 60% | Project | Students are graded on their individual contributions and overall quality of the project (see below) |

Given that this is a practicum, the project is central to this course and is worth 50% of your final grade. We will be partnering with BU Spark! to work on a semester-long, machine learning project. Projects are sourced from external partners and are complex enough to provide students with real-world ML experience.
Given that this is a practicum, the project is central to this course and is
worth 60% of your final grade. We will be partnering with BU Spark! to work on a
semester-long, machine learning project. Projects are sourced from external
partners and are complex enough to provide students with real-world ML experience.

### Project Grading

Projects will be graded on:
Projects will be graded based on a combination of overall project outcome and
individual contributions.

<table>
<tbody>
Expand All @@ -263,48 +265,45 @@ Projects will be graded on:
<th> Description/Notes </th>
</tr>
<tr>
<td> 30% </td>
<td> Objectives Achieved </td>
<td> Ultimately the goal of the project is to deliver towards the client's expectations. Did the project accomplish a sufficient number of (possibly revised) objectives?</td>
<td> 40% </td>
<td> Project Impact and Success </td>
<td> Ultimately the goal of the project is to deliver towards the
client's expectations.
<ul>
<li> Did the project accomplish a sufficient number of (possibly
revised) objectives?</li>
<li> Was the client relationship managed well? </td>
</ul>
</tr>
<tr>
<td> 10% </td>
<td> Client Relationship & Management </td>
<td> 20% </td>
<td> Repo Software and Documentation Quality, Reproducibility </td>
<td>
<ul>
<li> Did the team develop a positive, constructive rapport with the clients?</li>
<li> Did the team clarify client expectations and problem definition sufficiently?</li>
<li> Were project deliverables defined and updated with client input and approval?</li>
<li> Is the Github repository well organized and easy to navigate?</li>
<li> Is the repo well documented especially with replication steps?</li>
<li> Can one start from a new environment and easily setup and run?</li>
</ul>
</td>
</tr>
<tr>
<td> 25% </td>
<td> Algorithmic Soundness </td>
<td>
<ul>
<li> Is the training process well documented and reproducible?</li>
<li> Is the evaluation process rigorous and reproducible?</li>
<li> Is the dataset available and any preprocessing or augmentation well documented? </li>
</ul>
</td>
</tr>
<tr>
<td> 25% </td>
<td> Code quality, organization and reproducibility </td>
<td>
<td> 30% </td>
<td> Individual Contribution </td>
<td> Is there clear evidence of
<ul>
<li> Is the source code for the application well-structured with meaningful folder structure and intuitive filenames?</li>
<li> Is the environment documented and easily recreated?</li>
<li>n Is there a top-level README that orients the user to the repository, installation and operating instructions and summarizes results?</li>
<li> Is the project replicable? Has a clean installation been attempted?</li>
<li> Attendance and active participation in class lab time, client and team meetings?</li>
<li> Documented activities in sprint plan history?</li>
<li> Git commit history and co-authored git commits? </li>
<li> Record of individual's contributions in document and presentation revision history?</li>
</ul>
</td>
</tr>
<tr>
<td> 10% </td>
<td> Team Process </td>
<td> Did the team meet periodically and operate in sprints? Was project progress incremental? </td>
<td> Individual contribution to collaboration and teamwork</td>
<td> Is there indication, for example from peer reviews, of positive
collaborations and constructive teamwork?
</td>
</tr>
</tbody>
</table>
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