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MATH1005 variables

创建日期：2024-05-29 17:40:47

所属分类：数学mathematics

标签： MATH1005

variables

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MATH1005/MATH6005 Final Exam

Marks Description
2 Demonstrates mastery of how to use a truth table to determine whether or not a logical
equivalence holds by correctly using a truth table, or truth tables, to show that this logical
equivalence holds.
1 A minor error but demonstrates knowledge of how a truth table may be used to show that a
logical equivalence holds
0 Otherwise
Problem 1b
Marks Description
2 Demonstrates mastery of writing the structural part of an argument that proceeds via the
contrapositive. Introduces the variable G. Makes the correct hypothesis. Makes the correct
conclusion (may give a statement that is logically equivalent to the one listed in the solutions).
1 A minor error (like forgetting to introduce G, or supposing that the chromatic number of G is
greater than or equal to 4), but demonstrates knowledge of how to structure an argument via
the contrapositive. At an absolute minimum the response contains p->q and ~q->~p.
0 Otherwise
Problem 1c
Marks Descriptions
1 Demonstrates mastery of the notation of sets and cartesian products by correctly answering the
question and using notation accurately throughout
0.5 A minor error such as forgetting the empty set but must use notation correctly
0 Otherwise
Problem 1d
Marks Descriptions
3 An excellent element proof. Introduces variables, uses notation correctly (uses ⇔ between
statements, never = between statements, etc), justifies each step with definitions and laws, and
does not skip steps.
2.5 Almost an excellent element proof. As for 3 but doesn’t introduce at least some of the
variables or does not justify every step or skips an important step.
2 Pretty good element proof. Perhaps makes at least two of the errors described in 2.5.
1 Demonstrates some understanding of what to do, but misuses notation in a significant way (like
writing = between statements, writing ⇔ between sets, proving only one containment) OR only
draws the Venn diagrams
0 Otherwise
Problem 1e
Marks Descriptions
2 Describes an explicit counterexample and demonstrates that it is a counterexample
1.5 Correctly identifies that the statement is false, describes an explicit counterexample but does
not demonstrate that it is a counterexample.
1 Correctly identifies that the statement is false, but does not provide an explicit counterexample
0 Otherwise
Problem 2a
Marks Descriptions
3 An excellent induction. The induction structure is explicit (does not have to be exactly like the
solutions, but a clear base step and inductive step, explicit inductive hypothesis and invokes the
principle of induction) and explicitly states when the inductive hypothesis is used. The inductive
step is effective.
2.5 Almost an excellent induction. As for 3 but a minor error or omission.
2 A pretty good induction. Perhaps cannot make the inductive step work, but structures the
argument well.
1.5 Cannot make the inductive step work, and the structure has a minor error.
1 Demonstrates some understanding of mathematical induction, but does not rise to the level of 2
0 Otherwise
Problem 2b
Marks Descriptions
1 Effectively invokes generalised pigeon hole principle, or the pigeon hole principle, to explain
why there is at least one PIN shared by four or more customers.
0.5 Mentions the pigeon hole principle, but the proof is not quite effective, or makes mention of
pigeons and pigeon holes without making explicit reference to the pigeon hole principle.
0 Otherwise
Problem 2c
Marks Descriptions
3 An excellent computation that demonstrates mastery of probability when outcomes are equally
likely. Explicitly justifies the use of formula P(E) = |E|/|S|, and explains how to correctly count the
sets to be counted.
2.5 Almost an excellent computation. As for 3 but omits the justification that counting can be used
to compute the probability, or part of the explanation needs expanding.
2 A pretty good computation. Perhaps a minor error in one part of the computation, or correct
computation but almost no explanation and no justification as to why counting can be used
1 Demonstrates some understanding of how to use counting to compute a probability.
0 Otherwise
Problem 2d(i)
Marks Descriptions
1 Demonstrates mastery of the ideas of how to think through a probability experiment by
explaining how each ordered pair represents what happened in the experiment and
correctly describing the outcomes as a set of 16 ordered pairs
0.5 Demonstrates some understanding of the ideas of how to think through a probability
experiment, perhaps by correctly describing the outcomes as a set of 16 ordered pairs.
0 Otherwise
Problem 2d(ii)
Marks Descriptions
2 Demonstrates knowledge of what it means for events to be independent by correctly computing
the three probabilities required and determining that the events are not independent.
1 Demonstrates some understanding of what it means for events to be independent, perhaps by
giving a correct definition or by computing (perhaps incorrectly) the probabilities of E1, E2 and
their intersection
0.5 Does not demonstrate understanding of what it means for events to be independent but
correctly computes the probabilities of E1 and E2.
0 Otherwise
NOTE: GRADES FOR 2d(i) and 2d(ii) are combined for entry in spreadsheet
Problem 3a
Marks Descriptions
3 Demonstrates mastery of the vocabulary of walks, paths and circuits by giving three correct
answers (numbers and lists) and walks are described as alternating lists of vertices and edges
(it is necessary to list the edges because there are parallel edges in the graph).
2 Demonstrates a pretty good understanding of the vocabulary of walks, paths and circuits, but
for a minor error (omitting the trivial circuits in (iii) or not having the same circuit starting at
different points in (iii) or lists the correct number of walks IN EACH CASE but writes walks as a
list of vertices without the edges in between
1.5 Demonstrates a good understanding of the vocabulary of walks, paths and circuits but writes
walks as a list of vertices without the edges in between (it is necessary to list the edges
because there are parallel edges in the graph) and therefore does not distinguish between
some walks that are different (would lead to answers infinitely many, 1, 8)
Answers to only two parts from three parts for instance (i) and (ii) are correct.
1 Demonstrates some understanding of walks, paths and circuits, but does not rise to the level of
2 or 1.5. Answers to only one part of the three parts are correct.
0 Otherwise
Problem 3b
Marks Descriptions
1 Mentions something about arranging CPUs for the purpose of parallel computing
0 Otherwise
Problem 3c(i)
Marks Descriptions
1 Demonstrates knowledge of the vocabulary of graph isomorphisms by stating a correct
definition (may leave out “for all u_1, u_2 \in V(G)” as we left this out in the lecture slides)
0 Otherwise
Problem 3c(ii)
Marks Descriptions
2 Demonstrates mastery of enumerative combinatorics and graph isomorphisms and knowledge
of the complete bipartite graph by correctly counting isomorphisms and explaining the
reasoning.
1 Demonstrates understanding of what the complete bipartite graph is, and some idea that an
isomorphism must map vertices of degree m to vertices of degree m. May have the incorrect
answer.
0.5 Demonstrates understanding of what the complete bipartite graph is
0 Otherwise
Note: Score for 3c(i) and 3c(ii) combined for entry into the spreadsheet.
Problem 3d
Marks Descriptions
3 Demonstrates mastery of Hamilton circuits and proofs by providing a well-structured and
effective argument that the graph has no Hamilton circuit.
2 Pretty good argument. As for 3 but omits one or more than one necessary justification. No
false statements are made.
1 Demonstrates knowledge of what a Hamilton circuit is.
0 Otherwise
Problem 4a
Marks Descriptions
1 Demonstrates knowledge of the Nearest Neighbour Algorithm by “correctly” listing the input and
output. To be correct, the input must list “weighted complete graph” and the output must list
“Hamilton circuit for G” and must NOT list other things
0.5 If missing “complete” for input and otherwise satisfying for 1
0 Otherwise (including if the response claims that the algorithm produces a minimal weight
Hamilton circuit)
Problem 4b
Marks Descriptions
1 Demonstrates knowledge of Fleury’s algorithm and Euler circuits by correctly identifying the
condition.
OR
If they mentioned at most 2 vertices are odd and must start at an odd degree vertex
0.5 If they mentioned at most 2 vertices are odd (but not starting at odd)
0 Otherwise
Problem 4c
Marks Descriptions
2 Demonstrates understanding of Kruskal’s algorithm by correctly naming it and drawing the
correct minimal spanning tree.
1 Meets 1 of the 2 criteria for a 2
0 Otherwise
Problem 4d
Marks Descriptions
3 Demonstrates understanding of Dijkstra’s algorithm by producing lists that are correct and
complete.
2.5 3 except no A in path
2 Demonstrates a pretty good understanding of Dijkstra’s algorithm but at least one of the lists is
incorrect or incomplete. There is evidence that the misunderstanding is a minor error or minor
misunderstanding.
1 Demonstrates some understanding of Dijkstra’s algorithm but at least one of the lists is incorrect
or incomplete
0 Otherwise
Problem 4e
Marks Descriptions
3 Demonstrates understanding of the vertex labelling algorithm by producing lists that are correct
and complete.
2 Demonstrates pretty good understanding of the vertex labelling algorithm but at least one of the
lists is incorrect or incomplete. There is evidence that the misunderstanding is a minor error or
minor misunderstanding.
1.5 Demonstrates some understanding of the vertex labelling algorithm but at least one of the lists
is incorrect or incomplete AND with understanding of virtual flow.
1 Demonstrates some understanding of the vertex labelling algorithm but at least one of the lists
is incorrect or incomplete
(if the solution does not demonstrate understanding of virtual flow then it cannot score more
than 1)
0 Otherwise
Problem 5a
Marks Descriptions
2 Demonstrates understanding of transition diagrams for Markov process by drawing a correct
directed graph with correct labels, including correct deductions about the labels on edges for
which explicit information is not given in the problem.
1 Demonstrates some understanding of transition diagrams for Markov process by drawing a
directed graph with labels on edges. May have made an error in transcribing information from
the problem or may omit edges (and/or their labels) not explicitly mentioned in the problem.
OR
Makes a correct transition matrix instead of a transition diagram.
0.5 Draws a directed graph
OR
Makes a transition diagram
0 Otherwise
Problem 5b(i)
Marks Descriptions
1 Demonstrates an understanding of webgraphs, and the ability to model a similar situation, by
correctly describing the vertices and edges (including the correct direction).
0.5 Describes the vertices correctly
0 Otherwise
Problem 5b(ii)
Marks Descriptions
2 Demonstrates understanding of the two key hypotheses of the PageRank model by making
analogous statements about the employee graph (language may be rough, but there is enough
evidence to suggest an understanding of both)
1 Makes at least one of the two statements listed in the solution (language may be rough, but
there is enough evidence to suggest an understanding of at least one hypothesis)
0 Otherwise
Note: Scores for 5b(i) and 5b(ii) and combined for entry in spreadsheet.
Problem 5c(i)
Marks Descriptions
1 A correct webgraph
0.5 If added transition probability and dotted arrow for D
0 Otherwise
Problem 5c(ii)
Marks Descriptions
2 A correct basic transition matrix (also accept the same matrix but with ¼, ¼, ¼ , ¼ along the
bottom row, even though this is technically incorrect).

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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 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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 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 CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 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EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 MAS316/414 ACCT2522 MSIN0181 ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 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 IE5600 ECOS 3997 EDST2003 FIN 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