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CMT316 Applications of Machine Learning
创建日期：2024-05-20 18:09:02
标签： CMT316
Applications of Machine Learning

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 CMT316

 Applications of Machine Learning: Natural Language Processing and
Computer Vision

Assessment Title: Coursework 1
Assessment Number: 1
Date Set: Monday, 6th February 2023 (Week 2)
Submission Date and Time: by Monday, 6th March 2023 (Week 6) at 9:30am
Feedback Return Date: 3rd April 2023

Extenuating Circumstances submission deadline will be 2 weeks after the submission
date above
Extenuating Circumstances feedback return will be 2 weeks after the feedback return
date above

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.

Extensions to the coursework submission date can only be requested using the
Extenuating Circumstances procedure. Only students with approved extenuating
circumstances may use the extenuating circumstances submission deadline. Any
coursework submitted after the initial submission deadline without *approved*
extenuating circumstances will be treated as late.

More information on the extenuating circumstances procedure can be found on the
Intranet: https://intranet.cardiff.ac.uk/students/study/exams-and-
assessment/extenuating-circumstances

By submitting this assignment you are accepting the terms of the following declaration:
I hereby declare that my submission (or my contribution to it in the case of group
submissions) is all my own work, that it has not previously been submitted for
assessment and that I have not knowingly allowed it to be copied by another student. I
understand that deceiving or attempting to deceive examiners by passing off the work of
another writer, as one’s own is plagiarism. I also understand that plagiarising another’s
work or knowingly allowing another student to plagiarise from my work is against the
University regulations and that doing so will result in loss of marks and possible
disciplinary proceedings1.


In this coursework, students demonstrate their familiarity with the topics covered in the
module via two separate parts with equal weight (first part: 40%; second part: 60%).

Part 1 (40%)

In Part 1, students are expected to answer two practical questions.

1. Your algorithm gets the following results in a classification experiment. Please
compute the precision, recall, f-measure and accuracy *manually* (without
the help of your computer/Python, please provide all steps and formulas).
Include the process to get to the final result. (15 points)

Id Prediction Gold
1 True True
2 True True
3 False True
4 True True
5 False True
6 False True
7 True True
8 True True
9 True True
10 False False
11 False False
12 False False
13 True False
14 False False
15 False True
16 False False
17 False False
18 True False
19 True False
20 False False

2. You are given a dataset (named “real_state”) with different house properties
(dataset available in Learning Central). Your goal is to train machine learning
models in the training set to predict the house price of a unit area in the test
set. The problem should be framed as both regression and classification. For
regression, the house price of a unit area is given; for classification, there would
be two labels (expensive and not-expensive) depending on the house price of a
unit area: expensive if it is higher or equal to 30, and not-expensive if it is lower
than 30.
The task is therefore to train two machine learning models (one regression and
another one classification) and check their performance. The student can
choose the models to solve this problem. Write, for each of the models, the
main Python instructions to train and predict the labels (one line each, no need
to include any data preprocessing instructions in the pdf) and the performance
in the test set in terms of Root Mean Squared Error (regression) and accuracy
(classification). While you will need to write the full code to get to the results,
only these instructions are required in the pdf. (25 points)

Part 2 (60%)

In Part 2, students are provided with a text classification dataset (named “bbc_news”).
The dataset contains news articles split into five categories: tech, business, sport, politics
and entertainment. Based on this dataset, students are asked to preprocess the data,
select features and train and evaluate a machine learning model of their choice for
classifying news articles. Students should include at least three different features to train
their model, one of them should be based on some sort of word frequency. Students can
decide the type of frequency (absolute or relative, normalized or not) and text
preprocessing for this mandatory word frequency feature. The remaining two (or more)
features can be chosen freely. Then, students are asked to perform feature selection to
reduce the dimensionality of all features.

Note: Training, development and test sets are not provided. It is up to the student to decide
the evaluation protocol and partition (e.g., cross-validation or pre-defining a training,
development and test set). This should be explained in the report.

Deliverables for this part are the Python code including all steps and a report of up to 1200
words. The Python code is a single solved and run jupyter notebook (.ipynb) with clear
execution paths and comments, i.e. make sure to restart the kernel and execute each cell
such that all outputs and figures (if applicable) are visible. The code should take the
dataset set as input, and output the results according to the chosen evaluation protocol.
The code will consist of 25% of the marks for this part (15 points) and the report the
remaining 75% (45 points). The code should contain all necessary steps described above:
to get the full marks for the code, it should work properly and clearly perform all required
steps. The report should include:

1) Description of all steps taken in the process (preprocessing, choice of features,
feature selection and training and testing of the model). This description should be
such that one could understand all steps without looking at the code (15 points -
The quality of the preprocessing, features and algorithm will not be considered
here)
2) Justification of all steps. Some justifications may be numerical, in that case a
development set can be included to perform additional experiments. (10 points - A
reasonable reasoned justification is enough to get half of the marks here. The
usage of the development set is required to get full marks)
3) Overall performance (accuracy, macro-averaged precision, macro-averaged recall
and macro-averaged F1) of the trained model in the dataset. (10 points - Indicating
the results, even if very low, is enough to get half of the marks here. A minimum of
65% accuracy is required to get full marks)
4) Critical reflection of how the deliverable could be improved in the future and on
possible biases that the deployed machine learning may have. (10 points - The
depth and correctness of insights related to your deliverable will be assessed)

The report may include tables and/or figures.
Learning Outcomes Assessed

This coursework covers the six LOs listed in the module description. Specifically:
Part 1: LO1, LO2
Part 2: LO1, LO3, LO4, LO5, LO6

Criteria for assessment

Credit will be awarded against the following criteria.

➢ Part 1. The main criteria for assessment is based on the correctness of the
answers, for which the process is also required. Full marks will be given for
answers including both the correct answer and a correct justification or
methodology.

Part 1 – Q1 (15 points)
Precision
(4 points) • 0 point – no calculation process
• 1-3 points – depending on specific errors in calculation
process
• 4 points – Correct answer and correct calculation process
Recall
(4 points)
f-measure
(4 points)
Accuracy
(3 points)
• 0 point – no calculation process
• 1-2 points – depending on specific errors in calculation
process
• 3 points – Correct answer and correct calculation process

Part 1 – Q2 (25 points)
Classification
(12 points)
• 0 point – no Python instructions to train and predict the
labels
• 1-5 points – major errors in the Python instructions
• 6-11 points – minor errors in the Python instructions
• 12 points – good accuracy and correct Python instructions
Regression
(13 points)
• 0 point – no Python instructions to train and predict the
labels
• 1-5 points – major errors in the Python instructions
• 6-12 points – minor errors in the Python instructions
• 13 points – good Root Mean Squared Error (RMSE) and
correct Python instructions


➢ Part 2. This part is divided into Python code (25%) and an essay (75%). The code
will be evaluated based on whether it works or not, and whether it minimally
contains the necessary steps required for the completion of Part 2. Four items will
be evaluated in the essay, whose weights and descriptions are indicated in the
assessment instructions. The main criteria to evaluate those items will be the
adequacy of the answer with respect to what was asked, and the justification
provided.

Part 2 (60 points)
Python code
(15 points)
• 0 point – no/little code and comments
• 1-7 points – major errors/missing steps in the
code and comments
• 8-14 points – minor errors/missing steps in
the code and comments
• 15 points – the code runs and contain all
necessary steps and comments
Essay
(45 points)
Description
(15 points)
• 0 point – no description
• 1-7 points – mostly unclear descriptions of the
steps
• 8-14 points – mostly clear descriptions of the
steps but with some unclear ones
• 15 points – clear descriptions of the steps
Justification
(10 points)
• 0 point – no justification and development set
• 1-5 points – major issues in the justification
and/or the development set
• 6-9 points – minor issues in the justification
and/or the development set
• 10 points – Reasoned justification and good
use of the development set
Performance
(10 points)
• 0-5 points – no or missing performance
measures
• 6-9 points – reasonable performance
evaluation with accuracy <= 65%
• 10 points – good performance evaluation with
accuracy > 65%
Reflection
(10 points)
• 0 point – no reflection and discussion on
biases
• 1-5 points – major issues in the reflection
and/or discussion on biases
• 6-9 points – minor issues in the reflection
and/or discussion on biases
• 10 points – in-depth and correct reflection and
discussion on biases
案例分类
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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 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 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ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 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