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MATH-UA 132 006 economics
Creation date：2024-06-04 17:22:39
Belonging category：经济Economics
economics

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MATH-UA 132 006

This is the second semester of a sequence designed to give you the intuition to think about economic
ideas in mathematical terms and interpret mathematical concepts in the context of economics. Your
1
understanding of economics and mathematics both will improve after this sequence. Mathematics is
increasingly important in terms of the expression and communication of ideas in economics. A thorough
knowledge of mathematics is indispensable for understanding almost all fields of economics, includ-
ing both applied and theoretical fields. In particular, understanding of elements of calculus and linear
algebra are crucial to the study of economics, and this class is designed to provide such appropriate
mathematical tools. The formal derivations of the mathematical concepts needed will be the heart of
this class. Economic models can often be easily and precisely described in terms of mathematical nota-
tion when words and graphs would fail or mislead us. Therefore, as applications of the mathematical
concepts covered in class, examples and motivation will be drawn from important topics in economics.
Some key topics, roughly in order of their appearance in the course, include:
– multivariable functions;
– extrema and Lagrange multipliers;
– linear systems;
– matrix algebra;
– Riemann integral;
– integration techniques;
– differential equations.
By the end of Math for Economics II, students will have a complete understanding of optimization and
will be able to apply the Lagrange multipliers approach to constrained optimization problems. They will
also know how to solve systems of equations using linear algebra, and how to manipulate matrices. In
addition, they will be familiar with integration and various integration techniques. They will also under-
stand the basic concepts of differential equation and be able to find solutions of elementary differential
equations.
• RESOURCES AND PLATFORMS
– Books
∗ Required. Essential Calculus: Early Transcendentals (2nd ed.) by James Stewart. An electronic
version is included with WebAssign (see below), and you do not need to purchase a hard copy.
· Your course is participating in the Follett Access program. This is an NYU Bookstore initia-
tive that delivers required course materials at the lowest possible price. The required book
for this course, Essential Calculus by Stewart, will be delivered to you digitally through the
WebAssign platform. The cost of the platform & book is $108.00, which will be added
as a “book charge” to your bursar bill.
If you choose to find your course materials elsewhere, you must login here to the student
portal and opt out of the program by September 20th. If you do not opt out by this date,
you will be charged. *Please note - if you have paid for WebAssign & the Calculus
book through ACCESS in a previous semester you do not have to opt out, you will not
be charged.
∗ Recommended. Essential Mathematics for Economic Analysis (4th or 5th ed.), Sydsaeter & al.
– WebAssign
The class will use WebAssign for weekly assignments. WebAssign is an online platform that provides
computational exercises with immediate feedback. It can be accessed via Brightspace. WebAssign
requires a subscription that you can pay via the link on Brightspace. The subscription includes the
e-book. You can purchase an access code from the bookstore. By buying from the bookstore, you
can bill to your student account and use financial aid. You have three options: pay for one term,
pay for multi-term (if you intend to take MFE III), or pay for Cengage Unlimited, which is unlimited
2
access to all of Cengage’s texts. You will have a 2-week grace period to purchase and activate a
WebAssign license. If you already have a multi-term subscription from MFE I, you should be able
to use it.
– Campuswire
All communications will be handled through Brightspace and Campuswire. Announcements and
information on assignments will be posted there, so make sure to check both very regularly. You
should also ask any question regarding the class there (this can be anonymous). Your professor
and TA will periodically offer some input, but it should first and foremost remain a platform for you
and your classmates. For personal questions, you can send your professor or your TA a DM there.
Please allow up to two workdays for responses. Campuswire is accessible through Brightspace. You
will first need the code which is available by the Campuswire link on Brightspace.
– Gradescope
All assignments and some assessments will be administered through Gradescope. You will be able
to see your graded assignments and comments from the grader there. Gradescope is available
through Brightspace: you should use your NYU email address to register.
– Communication
The syllabus, course calendar, homework problem sets, and solutions will be uploaded to Brightspace.
All other communications will be handled through Campuswire, so make sure to check it very reg-
ularly. You should also ask any question regarding the class there (this can be anonymous). Your
professor and TA will periodically offer some input, but it should first and foremost remain a plat-
form for you and your classmates. For personal questions, send your TA or your professor a DM
there. As a general practice, please check your email a couple of times a day. For our course, make
sure you check Campuswire regularly, to make sure that your question hadn’t been answered there.
When messaging, please practice professional etiquette, including complete sentences and correct
grammar.
• GRADING POLICY
– Grading Scale1
Grade Score Range
A 93–100%
A– 90–92.99%
B+ 87–89.99%
B 83–86.99%
B– 80–82.99%
C+ 75–79.99%
C 65–74.99%
D 50–64.99%
F 0–49.99%
– Grade Distribution (Quick Reference)
1Incomplete Grades: University policy states that an incomplete grade may be awarded if the student is unable to complete their
work on time due to circumstances beyond their control. Please refer to the academic policies page for more information.
3
Part Weight
Attendance and Participation 10%
WebAssign 10%
Written HW 10%
Quizzes 10%
Midterm Exams 30%
Final Exam 30%
– Grade Distribution (Details)
∗ Attendance & Participation (10%)
Attendance is expected and required during lecture. This will be assessed sporadically in
lecture via real-time polls that will constitute 5% of your final grade. Those will be graded
mostly on effort and completion, and on correctness wherever applicable. More information
will be provided in lecture on the first day, but you cannot get the grade for those unless you
are present and active at the time when they are administered. However, there will be plenty of
opportunities to earn full points on this, even if you must skip class when one is administered.
In case of an impending health problems, personal issues, or religious observances, you may
skip class. You do not need to message me every time you skip class, but the total number of
absences that will not affect your attendance and participation score is 6.
In recitation, attendance is expected and required. Your TA will take attendance via a tradi-
tional sign-up sheet. You are allowed 3 free recitation absences without penalty. Every absence
after the 3rd one will result in a 0.5% deduction. No additional considerations will be made
so please use your absences wisely.
∗ WebAssign (10%)
There will be weekly WebAssign assignments for the sections of the textbook covered in lecture,
due by midnight on days indicated on the schedule. These exercises are usually computational,
and to be done after attending lecture and/or watching the available optional videos. You only
need to submit one answer, and you get feedback immediately. There are at least 3 attempts
for each question, and the lowest WebAssign grade will be dropped. No further extensions or
considerations are given, no exceptions.
∗ Written HW (10%)
There will be a total of 6 weekly written homework assignments with deadlines posted on the
course schedule, typically midnight on some Mondays. You can find the assigned exercises
and due dates on Brightspace. Please upload the HW by the deadline via Gradescope. Late
assignments will be accepted based on the policy stated on the cover page of each HW (the
same for every Written HW, but not for WebAssigns). In addition, the lowest Written HW grade
will be dropped. No further exceptions are made. See the HW guidelines at the end of this
document.
∗ Quizzes (10%)
You will take weekly quizzes (generally timed for 30 minutes) due by midnight on Sundays as
indicated on the schedule. Your lowest 2 quiz grades will be dropped. No further extensions
or considerations, no exceptions.
∗ Midterm and Final Exams (60%)
There are 2 exams and a final in the course. Since the learning goals are different and slightly
unrelated, the weights are as follows: 15% for Midterm I, 15% for Midterm II and 30% for the
non-cumulative Final.
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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 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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 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CMP7000A CMP-7038B Math 263 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 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. 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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