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Advanced Algorithms COMP4121
创建日期：2024-04-22 17:26:40
标签： COMP4121
Advanced Algorithms

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Advanced Algorithms COMP4121

Topics for the Major Project

• Abbreviation MC refers to the book “Networked Life” by Mung Chiang.
• Abbreviation KT refers to the COMP3121 textbook Algorithms Design by
Kleinberg and Tardos.
Here are a few topics for those undecided about what to do as a Major Project
for this class (50% of your class mark). You can choose several kinds of projects.
Here are some possible choices.
(I) You can choose the topic yourselves, in consultation with me. That would
be the best choice, choosing something which you are personally interested in,
preferably in a field that you would like to make a career in. If you have lined
up a job already, talk to your future manager and ask them for a project related
to what you will be working on. I’ll keep your project report confidential if
your future employer requests that. You can also choose to implement an app
that you have an idea for, in any field.
(II) You can write a scholarly essay, explaining a topic on the level equal to
my lectures or in more detail. Here are a few options.
(1) Local Search. See pages 661-700 in KT. This is an important technique
for finding an approximate solution to problems which are too hard to
solve exactly in a reasonable amount of time. For a high mark, include
solutions of the exercises at the end of this chapter of KT.
1
(2) Random Walks and Markov Chains See pages 76-116 in BHK. This
is an extremely important material of which the PageRank is probably
the most important example. For a high mark include solutions to a few
exercises at the end of that chapter of BHK.
(3) Random Graphs See pages 245-299 in BHK. For a high mark include
solutions to a few exercises at the end of the chapter.
(4) Topic Models, Nonnegative Matrix Factorisation, Hidden Markov
Models, and Graphical Models See pages 310-355 in BHK. These
are extremely important topics for those interested in advanced Machine
Learning. For a high mark include solutions to a few exercises at the end
of this chapter of BHK.
(5) Compressed Sensing and Sparse Vectors. See pages 360-373 in BHK.
A relatively new field important for dealing with massive data. For a high
mark include solutions to a few exercises at the end of that chapter of
BHK.
(6) Wavelets. See pages 385-402. An important method for signal represen-
tation, used for example, in JPEG2000. An alternative to the standard
sinc-based signal representation. For a high mark include solutions to a
few exercises at the end of that chapter of BHK.
(7) How does Google sell ad spaces? See pages 25 - 44 in MC. As an
alternative, choose any chapter from MC and extend it with more details
by finding relevant sources using Google.
(8) You can start reading “A first course in Machine Learning” by Simon
Rogers and Mark Girolami. If you have not taken a machine learning class
already, you are making a huge mistake - this is where most of the future
jobs will be, especially future good jobs. Then summarise the part you
managed to cover during the next couple of weeks. But, by all means, for
your own good, finish reading the whole book (after the trimester is over
and you are done with exams). In my opinion, this is by far the best
introduction to Machine Learning. It accomplishes something I would
think impossible: it assumes essentially only high school mathematics and
no statistics background, and yet, by introducing math, probability and
statistics as needed, it manages to do an entirely rigorous intro to Ma-
chine Learning. No black boxes whatsoever, goes fairly deep and is really
enjoyable to read. You can get a Kindle copy on Amazon and it will be
one of the best investments you have ever made!
2
(III) You can choose to implement yourselves a few algorithms for the
same task and compare their performance Examples include:
(1) Implement several clustering algorithms and apply them to synthetic
and real life data sets. 
(2) Implement several regression algorithms (such as the Ridge Regres-
sion or LASO) and apply them to synthetic and real life data sets
(3) Implement several classification algorithms and apply them to syn-
thetic and real life data sets

Marking: There is no strict requirement how many pages your report should
have; as a rough guideline, it usually takes 25-30 pages to present a topic well. Your
submission must be self contained, and someone who reads it should be able to learn
at least as much as you would learn by studying one topic which I have covered in
class and to the same depth level or higher. You should include the bibliography,
referencing any sources you have used. If you cite something verbatim, state so
and give a reference. The main criterion in marking will be how profitable
your project is for the advancement of your own knowledge and skill. This
includes being able to document what you have learned and what you have done. I
do not want you to do something just to pass the course; it has to be of real use to
you and to anyone who might read your submission. 
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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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