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CMT307 Applied Machine Learning
创建日期：2024-04-10 17:27:35
标签： CMT307
Applied Machine Learning

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Module Code: CMT307

Module Title: Applied Machine Learning
Assessment Title: Coursework 2 Machine learning project
Assessment Number: 2
This assignment is worth 50% of the total marks available for this module. If coursework is
submitted late (and where there are no extenuating circumstances):

1 If the assessment is submitted no later than 24 hours after the deadline,
the mark for the assessment will be capped at the minimum pass mark;
2 If the assessment is submitted more than 24 hours after the deadline, a
mark of 0 will be given for the assessment.

This will apply to any of the three parts to be submitted as part of this assignment.

Your individual submission must include the official Coursework Submission Cover sheet,
which can be found here:
Submission Instructions

This coursework submission consists of a group submission (note: group submission refers
to the submission of the three files for Part 1 and Part 2 hereafter) and an individual
submission. The group submission will be submitted in Learning Central by a nominated
team member, and the individual submission will be submitted in Learning Central by
individuals.

The group submission (from Part 1 and Part 2 of this assignment) consists of three files:
1. A single PDF file for your group report (up to 4500 words) on a specific machine
learning project.
2. A zip file containing all source code of your group project.
3. A single PDF file for the slides of your group presentation, which should include the
link to the video of the recorded group presentation on the first slide.
All group members must have seen and agreed to the final version of the submission.

The individual submission (from Part 3 of this assignment) consists of a single PDF file for
the self-reflection and peer assessment proforma.
2
Description Type Name
Part 1
(group
submission)
Compulsory One PDF (.pdf) file for group report (up to 4500 words) groupreport_[group number].pdf
Compulsory One ZIP (.zip) file containing the Python code Groupcode_[group_number].zip
Part 2
(group
submission)
Compulsory One PDF (.pdf) file for the presentation slides which also contains
the link to the video of your group presentation.
groupslides_[group_number].pdf
Part 3

(individual
submission)
Compulsory One PDF (.pdf) file for the individual peer assessment proforma peerassessment_[student number].pdf
Compulsory One PDF (.pdf) file for Cover sheet (to be individually submitted
with peer assessment proforma)
[student number].pdf

Note: This coursework consists of three part: Part 1 and Part 2 are for group report and
presentation. Part 3 is for individual work.

Part 1: Group report and project code. The deliverable includes a zip file with the code, and
a a single PDF file for the written summary (up to 4500 words) describing solutions, design
choices, evaluation and a reflection on the main challenges faced during development and
insights gained throughout the process. Prior to handing in make sure all documentation has
been collected. Additional supporting material, such as sources or data may also be submitted
if appropriate along with the code zip file. Any code submitted will be run in Python 3 and
must be submitted as stipulated in the instructions. Make sure the report clearly mentions
your group number, the project title, and the name of supervisor and a list of student ID
numbers of all members of the group on the title page of your report.

Part 2: Group presentation. The slides for the presentation should be submitted as a single
PDF document in learning central (group assignment) by the same nominated team member
as in Part 1. The link to the video of the group presentation should be given on the first slide
of the presentation.

Part 3: Peer assessment. Part 3 consists of a peer assessment proforma where students
reflect on individual contributions and assign marks to other members in your group. Each
individual will submit a cover sheet together with the peer assessment proforma in Learning
Central by the deadline.

Any deviation from the submission instructions above (including the number and types of
files submitted) will result in a reduction of 20% of the mark.

Staff reserve the right to invite students to a meeting to discuss coursework submissions
Assignment

In this coursework, students demonstrate their familiarity with the topics covered in the
module via a group project. This coursework consists of three parts: Part 1 and Part 2 are for
group report and presentation. Part 3 is for individual work.

Marks will be awarded to the individual student based on the quality of the group report, the
presentation and their contribution.


Part 1: Group report (70%)

In Part 1, students will be allocated in groups to design a machine learning project in one
specific topic. The list of all topics along with their descriptions is available in Appendix A.

Each group is given a specific dataset and a supervisor. The task of each group consists of
developing a whole machine learning pipeline that attempts to solve the task. The usage of
neural networks as methods/baselines is not mandatory but will be positively assessed; the
non-usage of neural methods should be properly justified.

Throughout the course the groups will have several milestones and should present their
progress to their supervisor in each session. Finally, the group will write a report summarizing
the steps followed and the main insights gained as part of the process.

As part of the group decisions, each student will be allocated to one of the following tasks:

- Descriptive analysis of the dataset + Error analysis
- Preprocessing + Literature review
- Implementation + Results

Each of these tasks will have a minimum of two students involved (except in exceptional cases
when this is not possible), who will work together in the specific task and as part of the group.
The structure of the report will be decided by the group members. In Appendix B, students
can find some guidelines to write the report, including some of the common sections that
groups may want to include in their report.

Note: These are just guidelines and students are not forced to follow this structure. New
sections may be added or adjusted if necessary.

Each student will also be involved in all group activities/tasks and will be responsible for the
well functioning and coordination of the team members.
Deliverables
The deliverables for this part include a report of no more than 4500 words and a zip file with
all the Python code and a README file. The group report must have the first page from
Appendix C. The code and README should contain three specific parts:

(1) Code to get the statistics used to complement the descriptive analysis of the dataset.
(2) Code to train one of the best performing models in the training set and evaluate it in
the test set. This code should also include all steps for preprocessing the original
dataset, if it were necessary.
(3) A README file explaining how to run the code for each of the two parts.

The code will not be marked separately and will only be used as a complement to assess
specific parts of the report.

Assessment

The final mark for this part (70% of the total marks) will result from the following items:

- Descriptive analysis of the dataset + result analysis (17%)
- Preprocessing + Literature review (18%)
- Implementation + Results (18%)
- Group report as a whole, including its coherence and structure (17%)

Note: Normally every member of the group will receive the same mark for the group report
and presentation, in some cases marks might be weighted by the individual contribution in
the project. This would be based on peer assessment for which instructions will be given in
Part 3.
案例分类
标签云
关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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