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CS5014 Machine Learning
Creation date：2024-05-06 18:06:31
Belonging category：计算机computer science
Tag CS5014
Machine Learning

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CS5014
Machine Learning

EXAM DURATION: 2 hours


EXAM INSTRUCTIONS (a) Answer three questions.

(b) Each question carries 20 marks.

(c) Answer questions in the script book.



YOU MUST HAND IN THIS EXAM PAPER AT THE END OF THE EXAM.


PLEASE DO NOT TURN OVER THIS EXAM PAPER UNTIL
YOU ARE INSTRUCTED TO DO SO.


1. Support Vector Machines

(a) With help of a diagram, explain the concept of margin in binary classifiers and
explain why it is important.
[3 marks]
(b) Explain the differences between hard-margin and soft-margin classifiers.
[3 marks]
(c) Linear SVM and logistic regression are both linear classifiers. Explain the
differences between the two classifiers in terms of:

i. Loss functions. [3 marks]
ii. Optimisation strategies. [4 marks]

(d) What is the relationship between Support Vector Machines and regularisation?
[3 marks]
(e) The function (, ′) = (1 − ⟨, ′⟩) is known as the polynomial kernel.
Explain its relevance to Support Vector machines: why is it useful, and how is
it used.
[4 marks]

[Total 20 marks]

Page 3 of 6

2. Basis Expansions

(a) What are basis functions and why are they useful in machine learning?
[3 marks]
(b) Using a diagram, give an example of polynomial regression.
[3 marks]
(c) You are given training data, from which you obtain the following scatter plot:


Explain the process of fitting a curve (regression model) to these data. Your
solution should give the general form of the equation which defines your
model (). You do not need to calculate the values of the parameters of your
model.
[6 marks]
(d) What is a cubic spline and how does it relate to basis expansion? Illustrate your
answer using a diagram.
[5 marks]
(e) Explain how the regularisation parameter influences L1-regularised
polynomial regression.
[3 marks]

[Total 20 marks]

3. Gradient Descent

(a) Describe what feature scaling is and why it is useful in machine learning.
[2 marks]

(b) A fellow machine learning researcher running gradient descent for a regression
task on a single dataset. They have produced three figures of the cost function
over number of epochs from three versions (V1, V2, and V3) of a model they
have produced:

i. Which version of the model (V1, V2 or V3) should they use and why?
[2 marks]
ii. For the versions of model that they shouldn’t use what would you
recommend they change in their algorithm and why?
[1 marks]

(Question continued on the next page…)

V1.) V2.)
V3.)
10,000 10,000
10,000
0 0
0
1,000 1,000
1,000
Epochs (iterations)
Cost
()
Epochs (iterations) Epochs (iterations)
0 0
0
Cost
()
Cost
()


3 Continued

(c) What are the advantages and disadvantages of using the normal equation
instead of gradient descent for a linear regression task?
[3 marks]

(d) What is the logistic sigmoid function? Your explanation should include a sketch
of the logistic sigmoid function, the logistic sigmoid function equation and
some text to describe it. Also explain why it is sometimes used in classification
tasks?
[3 marks]

(e) Using a linear logistic regression, the task is to classify recognition of 5 different
vehicles in 1000,000 images of classes: cars, boats, planes, trucks, and trains),
how can multiclass classification be done, explain the strengths and
weaknesses of the approach.
[3 marks]

(f) Describe the cost function used in gradient descent for logistic regression and
why it’s used. Include an equation of the cost function to be used in gradient
descent for logistic regression including regularisation. Describe the terms you
use and explain why there are in the equation.
[6 marks]


[Total 20 marks]

Page 6 of 6

4. Neural Networks

(a) Explain how a single neuron (logistic unit) is used in an artificial neural
network, your answer should include a sketch and explanatory text.
[3 marks]

(b) How many neurons are required, in the output layer to classify inputs of
multiple types of medical blood test data into cancer or not-cancer classes?
Your answer should also explain why.
[1 marks]

(c) Describe the forward propagation in neural networks for this task of classifying
cancer or not cancer using the inputs from three medical blood data features.
The network architecture you should use has the following properties: there is
an input layer, 2 hidden layers and an output layer, you get to decide the
number of neurons per layer. Your answer should use a sketch, any equations
used and explanatory text of the process.
[5 marks]
(d) What is the purpose of backpropagation in neural networks?
[2 marks]

(e) Design a machine learning program, utilising neural networks, which will
automatically decide whether buy, sell, or retain your stocks in oil company
EvilOil on the stock market. You are given hourly stock market data (stock
prices) from the last 3 years for all the companies (including EvilOil) that are
traded on major stock market exchanges all over the world, you are also given
data on the number of times that EvilOil is mentioned on social media sites,
again hourly over the 3 year period, you are allowed to use additional data you
can acquire if you wish. Your answer should include all the steps you take and
how you evaluate technique. You do not need to write the program, just the
steps that the program should take (and why).
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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 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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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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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