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  • Common Bugs in Writing
    7 shows is right Avoid excessive parenthesized remarks as they make the text hard to read fold into the main sentence Check whether the publication allows footnotes some magazines frown upon them More than two footnotes per page or a handful per paper is a bad sign You probably should have applied to law school instead The material should make just as much sense without the footnotes If the reader constantly has to look at footnotes they are likely to lose their original place in the text As a matter of taste I find URLs better placed in the references rather than as a footnote as the reader will know that the footnote is just a reference not material important for understanding the text There is no space between the text and the superscript for the footnote I e in LaTeX it s text footnote rather than text footnote Check that abbreviations are always explained before use Exceptions when addressed to the appropriate networking audience ATM BGP ftp HTTP IP IPv6 RSVP TCP UDP RTP RIP OSPF BGP SS7 Be particularly aware of the net head bell head perspective Even basic terms like PSTN and POTS aren t taught to CS students For other audiences even terms like ATM are worth expanding as your reader might wonder why ATM has anything to do with cells rather than little green pieces of paper Never start a sentence with and There are exceptions to this rule but these are best left to English majors Don t use colons in mid sentence For example This is possible because somebody said so is wrong the part before the colon must be a complete sentence Don t start sentences with That s because In formal writing contractions like don t doesn t won t or it s are generally avoided Be careful not to confuse its with it s it is Vary expressions of comparison Flying is faster than driving is much better than Flying has the advantage of being faster or The advantage of flying is that it is faster Don t use slash constructs such as time money This is acceptable for slides but in formal prose such expressions should be expanded into time or money or time and money depending on the meaning intended Avoid cliches like recent advances in and paradigm You do not want readers of your work to play buzzword bingo Other words should be banished Don t use symbols like for and for fraction or percentage or for follows or implies in prose outside of equations These are only acceptable in slides Avoid capitalization of terms Your paper is not the U S Constitution or Declaration of Independence Technical terms are in lower case although some people use upper case when explaining an acronym as in Asynchronous Transfer Mode ATM Expand all acronyms on first use except acronyms that every reader is expected to know In a research paper on TCP expanding TCP is probably not needed somebody

    Original URL path: http://www.cs.columbia.edu/%7Ehgs/etc/writing-bugs.html (2016-02-17)
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  • Standard References
    support of Supplementary Services in H 323 H 450 2 Call Transfer supplementary Service for H 323 H 450 3 Call Diversion supplementary Service for H 323 H 235 Security and Encryption for H series multimedia terminals hostmaster webmaster rfc2142 HTTP rfc2616 ICMP rfc792 IGMPv1 rfc1112 IGMPv2 rfc2236 IMAP draft crispin imapv rfc2060 IPv4 rfc791 IPv6 IPng rfc1883 IPsec framework rfc2401 intserv flowspec rfc2210 Intelligent networks Fayn97 Intelligent ISDN signaling Q 931 vanB98 Signaling ISUP vanB98 Signaling Host requirements rfc1123 Karn s algorithm Karn8708 Improving LDAP rfc1777 Howe97 LDAP rfc2798 persona LDIF rfc2849 lightweight conferencing architecture draft ietf mmusic confarch MADCAP rfc2730 MBONE Erik9408 MBONE Schu9502 Internet Casn9207 First MGCP rfc2705 Megaco rfc2885 rfc2886 MIME rfc1521 old rfc1341 mobile IP rfc2002 multicast Deer9005 Multicast multicast scoped rfc2365 multiplexing rfc1006 rfc1692 MX DNS rfc974 nevot Schu9207 Voice nv Fred9409 Experiences NAT NAPT draft ietf nat traditional NNTP rfc977 OSI 7 layer model Zimm80 OSI Day8312 OSI PEP Khar9701 PEP PIM Deer9409 Architecture playout delay Ramj9406 Adaptive Rose0003 Integrating Moon9801 Packet POP rfc1725 PPP rfc1661 RADIUS rfc2138 rfc2139 accounting RDP rfc1151 rescap router alert rfc2113 RTP rfc1889 rfc1890 RTP redundancy rfc2198 RTSP rfc2326 RSVP Zhan9309 RSVP rfc2205 rfc2208 applic draft ietf rsvp

    Original URL path: http://www.cs.columbia.edu/~hgs/netbib/std.html (2016-02-17)
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  • Citing URLs
    to track down invalid links For example when giving the URL for a preprint provide the document s full title along with the full details of the authors instead of say using et al Check all URLs particularly ones that have apparent embedded user information It is best for somebody other than the author to check these URLs In case of URLs that cite repositories controlled by the author Place material in a reliable central repository such as a preprint or software archive This is particularly important for links to complete versions of papers omitted proofs and supporting data or results Most academic institutions have a technical report series If one of the co authors is a member of such an institution strongly consider placing the full version of the paper there For preprints consider netlib org For open source software consider using the free services of sourceforge net Avoid using student accounts at all cost use pointers to research lab or group pages as they are often maintained even after the faculty or research staff person leaves an institution Name the repository and include the name in citations This name is available for later searches For software distributions include

    Original URL path: http://www.cs.columbia.edu/%7Ehgs/etc/urls.html (2016-02-17)
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  • Hints for PhD proposal defenses
    ingenuity of the speakers accomplishments what takes the work out of the routine Naturally these comments apply to all of our speakers who want to impress people with their ability as opposed to the breadth of their knowledge or the size of their project Ed Coffman When presenting experimental work be prepared to defend your methodology What was your sample size Confidence intervals Standard presentation guidelines apply Be prepared for example avoid wasting your audience s time with setting up the projector or conference bridge Thus try out any presentation arrangements well ahead of time particularly if it includes remote presentation multimedia contents or demonstrations Your first slide should be on the screen when the committee shows up for your talk Dress for the occasion This is a major milestone in your academic career where most members of your committee will meet you for the first time Treat it like a job interview It is customary but not required to provide minimal refreshments such as coffee and maybe some fruit and or cookies Talk to your audience not to your slides Project your voice speaking softly conveys the impression that you are unsure of what you are saying Make sure that all your graphs are readable Check this in the actual presentation environment using a video projector not just on your laptop screen A common problem is that the lines are too thin Avoid flashy or cheesy animations such as animated GIFs or PowerPoint word art This is not a sales talk and these gimmicks distract from the message and make you look unprofessional Keep to the allotted time of no more than 45 minutes Your presentation needs to address the following What is the problem you are studying Why is it important What results have you achieved so far and why to they matter How is this substantially different from prior work What do you need to do to complete your work Your workplan should be sufficiently detailed so that the committee can judge whether it is realistic or not You don t have to account for every day between the proposal and your thesis defense but a roughly monthly or quarterly granularity is to be expected depending on how far away your anticipated graduation date is Specify the experiments you need to run the software you need to write and the algorithms you want to try out This should not just be one page that says I will do miraculous things The committee should be handed a copy of your slides At least a day ahead email the presentation to your committee as well No more than 25 slides plus back up slides with additional material in case of questions The committee will get anxious once the presentation lasts longer than 35 40 minutes List your contributions early and explicitly You don t want to create the impression that related work is yours and vice versa One of the most important concerns during the proposal is to convince

    Original URL path: http://www.cs.columbia.edu/%7Ehgs/etc/proposal-hints.html (2016-02-17)
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  • cs4111-Introduction to Databases
    not have these prerequisites This course is intended for both Computer Science majors as well as non majors Topics The fundamentals of database design and application development using databases entity relationship modeling logical design of relational databases relational data definition and manipulation languages SQL object relational databases query processing physical database tuning transaction processing Textbook Raghu Ramakrishnan Johannes Gehrke Database Management Systems 3rd edition McGraw Hill 2002 ISBN 0072465638 Available from the Book Culture bookstore located at 536 W 112th St between Broadway and Amsterdam Ave Also on reserve in the Science and Engineering Library Grades 15 homework assignments 4 all equally weighted 20 projects 2 Project 1 is worth 15 of the course grade while Project 2 is worth 5 of the course grade Note for the projects students can choose between a programming option to be done in Python and a nonprogramming option 25 midterm closed book Thu Mar 10 in class 40 final closed book Thu May 12 1 10 4 00 p m Instructor Prof Luis Gravano Office 706 Schapiro CEPSR Office Hours Mondays 9 30 11 30 a m By appointment by email Telephone 1 212 939 7064 Email gravano cs columbia edu Instructional Assistants

    Original URL path: http://www.cs.columbia.edu/~gravano/cs4111/ (2016-02-17)
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  • COMS 4721 Machine Learning for Data Science (Spring 2016)
    the instructor or course assistants Nov 23 If you are not a DSI student you can put yourself on the waiting list It is scheduled to be processed in early January Nov 10 At present registration may only be open to DSI students It may be open to non DSI students closer to the start of the semester Schedule Topics and dates Reading Homework Course overview Jan 19 CML 1 1 1 2 ESL 1 2 1 2 3 notes on prereqs HW0 submit here due Jan 21 Nearest neighbors Jan 21 CML 2 2 2 3 2 5 ESL 2 4 2 6 13 3 slides on K D trees Decision trees Jan 26 CML 1 3 1 9 ESL 9 2 Probability statistics Jan 28 Grinstead Snell 9 1 CML 4 6 ESL 7 10 slides on cross validation HW1 due Feb 16 Generative models Feb 2 4 CML 7 1 7 5 ESL 4 3 4 3 1 4 3 3 optional Linear classifiers Feb 9 CML 3 6 1 ESL 4 4 4 4 4 optional 4 5 Voted Perceptron paper Features kernels Feb 11 CML 4 1 4 4 9 1 9 2 9 4 notes on kernels SVMs Feb 16 CML 6 1 6 7 ESL 4 5 2 12 1 12 3 4 SVM tutorial HW2 due Mar 1 Optimization Feb 18 23 25 Beyond binary outputs Ensemble methods Online prediction demo Regression Dimension reduction Clustering Mixture models Collaborative filtering Sequence models Partial feedback Course information and policies Topic Machine learning Prerequisites Multivariable calculus linear algebra probability Basic programming e g in MATLAB or Python data structures and algorithms General mathematical maturity Readings I will assign readings from notes books and research papers available on the web CML Daumé A Course in Machine

    Original URL path: http://www.cs.columbia.edu/~djhsu/coms4721-s16/ (2016-02-17)
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  • COMS 4772 Advanced Machine Learning (Fall 2015)
    Upcoming due dates Dec 7 HW4 due by 5 00 PM Dec 10 Final project report due by end of day send to course e mail Schedule Date Topics Reading Homework Sept 9 Course overview Sept 14 High dimensional Euclidean space Lec 1 of Ball Sec 2 1 2 4 of BHK Sept 16 Probability Sec 12 4 of BHK Notes on probability HW1 due Fri Oct 2 Sept 21 Probability random linear maps Dasgupta and Gupta Sept 23 Random linear maps subspace embeddings Notes on J L lemma Sept 28 Subspace embeddings approximate least squares Sept 30 Structured random linear maps Sec 2 of Ailon and Chazelle Oct 5 Spectral decomposition multivariate Gaussians Sec 2 6 2 9 12 6 of BHK Oct 7 Separating Gaussian populations projection pursuit Sec 2 8 of BHK HW2 due Fri Oct 23 Oct 12 PCA and SVD Notes on PCA SVD Oct 14 Matrix norms low rank matrix approximation Sec 3 4 3 6 3 9 of BHK Oct 19 Low rank matrix approximation power method Sec 3 7 3 10 of BHK Oct 21 Power method sketch and solve low rank approximation Oct 26 Sketch and solve low rank approximation k center clustering Notes on low rank approximations HW3 due Mon Nov 9 Oct 28 k means clustering k means Sec 8 1 8 2 of BHK Nov 2 No lecture Nov 4 k means divide and conquer k means clustering Aggarwal Deshpande and Kannan Nov 9 k means PCA Euclidean embeddings Sec 8 4 of BHK notes on k means Nov 11 Embeddings into ell 2 and ell infty Sec 15 4 of Matoušek Nov 16 Fréchet embeddings Sec 15 7 of Matoušek Nov 18 Embedding ell 2 d into ell 1 O d approximating sparsest cuts Sec 15 8

    Original URL path: http://www.cs.columbia.edu/~djhsu/coms4772-f15/ (2016-02-17)
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  • Foundations of Graphical Models, Fall 2015
    with comments and errors Introduction Reading Build compute critique repeat Data analysis with latent variable models Blei 2014 Slides A Quick Review of Probability Basics of Graphical Models Reading Conditional Independence and Factorization in Introduction to Probabilistic Graphical Models Jordan 2003 Elimination Tree Propagation and the Hidden Markov Model Reading The Elimination Algorithm in Introduction to Probabilistic Graphical Models Jordan 2003 Reading Probability Propagation and Factor Graphs in Introduction to Probabilistic Graphical Models Jordan 2003 Models data and statistical concepts Reading Statistical Concepts in Introduction to Probabilistic Graphical Models Jordan 2003 Reading Some issues in the foundations of statistics Freedman 1995 Optional reading Model based machine learning Bishop 2013 Bayesian Mixture Models and the Gibbs Sampler Reading Gibbs sampling for the uninitiated Resnik and Hardisty 2010 Probabilistic Modeling in Stan Reading Stan Reference Manual 2 8 0 Chs 1 5 1 9 Stan Development Team 2015 Exponential Families and Conjugate Priors Reading The Exponential Family Bishop 2006 Section 2 4 Mixed membership Models and Mean Field Variational Inference Reading Probabilistic topic models Blei 2012 Reading Inference of population structure using multilocus genotype data Pritchard et al 2000 Matrix Factorization and Recommendation Systems Reading Matrix factorization techniques for recommender systems Koren

    Original URL path: http://www.cs.columbia.edu/~blei/fogm/2015F/index.html (2016-02-17)
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