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COMP9414 Computer vision
创建日期：2024-09-02 15:17:30
标签： COMP9414
Computer vision

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TOPIC: Search – Question: IDDFS [4 marks]

Consider the following road map with distances indicated on lines drawn between towns (the map is not to scale). The straight-line distances form. each town to H are listed in the table.

What order are nodes expanded by iterative deepening depth first search when searching for a path between A and H? Where there is choice of nodes, take the first one by alphabetical ordering.

Assume the search algorithm includes cycle checking along a path, Tree-Search-IDDFS. Stop the search once the goal node is expanded.

a. A ABCD ABFCDEDCGH

b. A ABCD ABFCDH

c. A ABCD ABCDE ABFCDH

d. A ABCD ABFH

e. A ABCD ABFCDEGH

TOPIC: Search – Question: A* [4 marks]

Consider the following road map with distances indicated on lines drawn between towns (the map is not to scale). The straight-line distances form. each town to H are listed in the table.

What order are nodes expanded by A* search using the straight-line distances to H in the table as the heuristic function, when searching for a path between A and H? Where there is choice of nodes, take  the first one by alphabetical ordering. Stop the search once the goal node is expanded.

a. ADH

b. ABFH

c. ABCDEFGH

d. ACDH

e. ACEGH

TOPIC: Reinforcement learning – Question: Softmax [4 marks]

Consider an RL agent navigating a gridworld, with four possible action: up (U), down (D), left (L), and  right (R). The agent uses the softmax action selection method. Remember this method computes the probability of selecting an action using a Boltzmann distribution, as follows:

In a particular given state St, the agent has the following Q-values to decide what action to take next:

Q(U)

Q(D)

Q(L)

Q(R)

0.7698

0.6501

0.0252

-0.7698

What would be the action selected by the agent if the temperature T used is 0.9 and the random number drawn is 0.9021.

TOPIC: Reinforcement learning – Question: Returns [4 marks]

Consider the return equation shown below with a discount factor γ = 0.9 and a reward sequence of 7, 3, 1, 10, -10. The return G0  is equal to:

TOPIC: Neural networks – Question: Single-layer perceptron [4 marks]

Consider the training data shown in the following Table to divide the space. Using the single-layer perceptron learning rule with a learning rate α = 1.0 and initial weights w1  = 1, w2  = 0, and b = 1.5, what would be the final value of the weight after convergence?

Training example

x1

x2

Class

a

0.0

0.0

1

b

1.0

2.0

-1

c

2.0

-1.0

-1

d

-2.0

1.0

1

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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 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MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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101H1 IB9520 FINM 3008 ECON7310 FIT 1006 MANAGEMENT MATH3821 MTHM504 CS975 COMM1100 ELEC2133 MMAI 5500 MIS611 ELEC3114 CS 173 ELEC 3105 ISO 9001 IDSC10 Rural livelihoods and globalisati IDSC10 K17 202 CS823 MAST90007 SPG8024 MAT246 EG-264 CVEN2303 INFS1603 3PR3 MN-2508 4CCE1MCP SPG8030 CMT224 Inflation consumption Applying MAT137 CSE 111 DPBS1110 FINS5530 ELEC4613 GSBS6200 COMP4336 Math W54 DIFFERENTIAL 7PAVMALM ACC 1110 ME 3210 MATH2801 MGMT20001 ECON 6202 PSTAT 120B COMP9024 ENG5027 comp3219 ELE00107M ECON20003 ECON2103 FINA2209 MAST20018 LUBS5308M01 MATH2023 Computation COMP1600 CHEM 3X23 BBMM506 FNCE20003 BMET2922 BISM7216 financial ECMT5001 ACST3059 MAST30012 COMP3511 ECOS 2201 MKTG7512 ECON2540 MTH1010 MAT1841 COMP4600 ECON7740 PAPER C331 CAP 6617 ECON2420 ACCT7107 HLTH 7001 ECOS3002 CVEN30010 MAST30021 MATH1014 BIOL1007 ECOS 3010 SWEN90016 BISM7202 ACCT90012 INFO3315 DSCI 553 INFO90002 COMP3419 CITS 3001 EE5731 FINA3326 NFS 284 COMM2501 FIT2104 PHYS3035 RSM260 MAN3080 CHEM30028 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