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COMP0050 Coursework
Creation date：2024-05-21 14:28:14
Belonging category：计算机computer science
Coursework

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COMP0050 Coursework FAQ

• Is the page limit a hard constraint?
No, if you write slightly more than the allocated number of pages it is fine,
but you should not overdo it.
• Are figures and tables included in the page count?
Yes, figures are included in the page count. The idea being that you should
decide which figures and tables are important to convey your message.
One exception is if you have some very large figures or tables (e.g. one
that takes an entire page, typical examples are figures that show
correlations between the features of your dataset, or tables that contain a
detailed description of the dataset), in which case you can put it as an
appendix.
• Should references be included?
Yes, it is good practice to include references in a report. However, you
should include references that are relevant in the context of your report, not
just for the sake of having a list of references.
• Do references count for the page limit?
No, references are excluded from the page limit.
• Should an abstract be included?
This is optional, but it in general good to include a short abstract. This
would not count towards the page limit.
• How many methods should I use for my analysis?
There is no general answer to this. The coursework is relatively free for
what concerns its execution, and you can decide what specifically to focus
on. A possibility may be to use several methods and compare their
performance. At the opposite side of the spectrum of possibilities one could
focus on a single method and perform a very detailed analysis for that.

• The method I considered gives a poor result, am I going to be
penalized for that?
Negative results are fine. As long as the process you used is sound, if it
turns out a model has poor predictive performance you will not be
penalized because of that. Clearly, if your model has poor performance for
an obvious reason that could have been solved in the context of the
method you have selected, this would have an impact.
• Can I add an appendix?
You can put supporting material in an appendix, which does not count
towards the total number of pages. The appendix cannot however be used
as a substitute for the main text (e.g. one cannot put all pictures and tables
in an appendix and then systematically refer to them in the main text). The
main pictures and tables that are needed to understand results should be in
the main text. If you for instance have a very large picture that would take
up a lot of space, then it's okay to put it in the appendix. Similarly, if you
wish to add code, or you need large tables related for instance to the data,
those can go in an appendix.
In summary, an appendix is perfectly fine as long as it is used to add
supplementary material not necessary to understand the main text.

• Should I submit the code?

Only the report will be marked. You can upload the code if you wish, or add
it as an appendix, but it is optional.

• Should I use “I” or “We” in the report, as the latter appears more
formal in academic writing?

We would suggest the use of “I”, but both options are fine.
• Should I structure the report as Task 1 (introduction, methods,
results, etc) followed by Task 2 (introduction, methods, results,
etc, or should I combine Task 1 and Task 2 into the same
sections?

We would suggest the former (essentially merging two reports, one per
task, into the same document).
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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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