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COMP4318/5318 Assignment
创建日期：2024-06-05 16:46:35
标签： COMP4318
Assignment

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COMP4318/5318 Assignment 2

Key information
This assignment is worth 25% of your final mark. It is a group assignment to be
completed in pairs. Please ensure you have registered your pair on Canvas under the
People tab, in either Assignment 2 Groups Section A or Assignment 2 Groups Section B
per the announcements on Ed.
Please read the entire specification carefully before beginning the assignment, and refer
back to it while working on your project. Please take special note of the information
provided on Academic Integrity.
Deadline
11:59pm 20 October 2023 (Friday week 11)
Late submissions are allowed up to 3 days late, with a penalty of 5% of the maximum
possible mark per calendar day. Late submissions after 3 calendar days will not be accepted.
Submission information
Three files are required to be submitted in the relevant submission portals on Canvas:
- Your report as a .pdf file
- Your jupyter notebook as a .ipynb file
- Your jupyter notebook as a .pdf file
A pdf of your jupyter notebook can be generated using File>Download as>PDF or Print
Preview > Save as PDF.
Name your files with the following format:
- Report:
o A2-report-SID1-SID2.pdf
- Code:
o a2-code-SID1-SID2.ipynb
o a2-code-SID1-SID2.pdf
where SID1 and SID2 are the SIDs of the two students in your pair. Please do not include
your names anywhere in your submissions.
Please keep your report to a maximum of 12 pages of size 12 Times New Roman font
(additional pages past this limit will not be marked). You may include references and an
appendix with supplementary figures which are not included in this limit.
Code information
Your code for this assignment should be written in Python in a Jupyter Notebook
environment. Please follow the structure in the template notebook provided. Your
implementation of the algorithms should predominantly utilise the same suite of libraries we
have introduced in the tutorials (Keras, scikit-learn, numpy, pandas etc.). Other libraries may
be utilised for minor functionality such as plotting, however please specify any dependencies
at the beginning of your code submission. While most of your explanation and justification
can be included in the report, please ensure your code is well formatted, and that there are
sufficient comments or text included in the notebook to explain the cells.
You can choose to run your code locally or on a cloud service such as Google Colaboratory,
however your final submission should be able to run on a local machine. Please submit your
notebook with the cell output preserved and ensure that all results presented in your report are
demonstrated in your submitted notebook.Your code may also be rerun by your marker, so
please ensure there are no errors in your submitted code and it can be run in order.
Task Description
In this assignment, you will implement several machine learning algorithms to solve an
image classification task, and compare their theoretical properties and experimental results
thoroughly.
You will need to demonstrate your understanding of the full machine learning pipeline,
including data exploration, preprocessing, model design, hyperparameter tuning, and
interpreting results. Moreover, the assignment will require you to consolidate your knowledge
from the course so far to effectively discuss the important differences between the algorithms.
While better performance is desirable, it is not the main objective of the assignment. Rather,
it is important to fully justify your d
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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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