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CMP-7030Y Research Techniques

Creation date：2024-04-23 17:21:43

Belonging category：计算机computer science

Tag CMP-7030Y

Research Techniques

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Computing Sciences

Module: CMP-7030Y Research Techniques

Submission : The Module’s Blackboard
Learning outcomes
1. Generic: Being able to search the internet and library for the relevant resources, build a
bibliography, and plan written work.
2. Specific: Learn LaTex and Bibtex, and use them to write a substantial critical review
of published work on your chosen topic by focusing on evaluating and comparing the
research methodology used in each of the work you reviewed.
Specification
Overview
Generic: to Learn and practise skills for technical/scientific writing, bibliographic searching and
critical review and evaluation.
Specific: to write a technical review report (using LaTex) on a specified topic chosen from the
given list of the suggested topics in computing sciences.
Plagiarism:
Plagiarism is the copying or close paraphrasing of published or unpublished work, including
the work of another student without the use of quotation marks and due acknowledgement.
Plagiarism is regarded a serious offence by the University and all cases will be reported to
the Plagiarism Officers for further investigation. Work that contains even small fragments of
plagiarised material will be penalised.
Description
Task: Choose ONE topic from the given topic list (issued later separately via Blackboard), do
the work as specified in the topic description, and write a technical report. More details are
given on the topic page.
Relationship to formative assessment
An outline of your study report must be submitted via Blackboard by Week 6 (details will be
issued later via Blackboard).
1
School of Computing Sciences
Deliverables
A technical report is required to be written in plain English the form of a scientific review paper,
that can be understood by readers who have a background in computer science, data science
or related areas but who are not necessarily experts in the topic. It must not exceed 3500
words, excluding the abstract and reference list, and must be formatted using LaTex and Bibtex
(which will be taught in the lectures and practised in the laboratory exercises).
Submissions and Handing in Procedure:
1. You are required to submit a pdf file of your report to the “Dropbox” on the Module’s Black-
board by the submission deadline. Note: late submission is subject to penalty.
2. In addition, all your working files used in producing your report (such as .tex, .bib, and figures
etc) need to be zipped together and submitted to another folder in the “Dropbox” (the details of
electronic submission will be issued later).
If you have medical or other problems you may apply for an extension to your deadline by
filling an application form on evision. However, it is essential that you obtain proper support
documents (e.g. medical note) and send them to the PGT Hub by emails.
Marking scheme
The following schemes will be applied in marking:
1. Writing tools, report structure and English language (30%): your work should demonstrate a
competence in the use of LaTex, i.e. using a range of different LaTex commands and advanced
functions. The report should be logically structured using sections and sub-sections, such as
Abstract, Introduction, etc. and well written with good academic English language. The main
sections should be logically organised such that they have a beginning, middle and end, the
latter should include pointers to previous or following sections if applicable.
2. Synthesis and evaluation of relevant literature (30%): Your work should show evidence of
critical reviews, e.g. comparison and contrast approaches should be employed when reviewing
and summarising the work of others and/or your ideas.
3. Explanation precision and coherence (30%): Ideas must be developed and presented cor-
rectly and coherently to demonstrate your understanding of the area of your chosen topic.
Concepts and terms are clearly explained. Jargon and colloquial language should be avoided
as much as possible.
4. Use of references (10%): evidence of having consulted a reasonable number of relevant
articles from a wide range of sources (books, internet, journals, conferences). References are
presented by using the Harvard citation style. Where quotations of work published elsewhere
are included in your report, these must be clearly indicated. Tips: you should try to avoid using
too much quotation unless really necessary.
More detailed marking scheme can be found from the marksheet available in the same folder.
Resources
Use the lecture slides and lab exercise sheets and provided sample files to start your course-
work.
In addition, there are plenty of resources on the internet on literature review and technical
report writing.

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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 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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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