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====== CSCI5510 Big Data Analytics ======
 [ [[:teaching:csci5510:discussions:2013|Discussion Forum]] | [[:teaching:csci5510:blogs:2013|Blogs]] ]

==== Breaking News ====

  * <hi #ffff00>**September 2, 2013**.</hi> The new semester begins.

==== Extra Credit Assignments ====

===== 20013-14 Term 1 =====


|  ^  Lecture I  ^  Lecture II  ^  Tutorial I  ^  Tutorial II  ^
^ Time    |  M2-4, 9:30 am - 12:30 pm  |    |  TBA  |   |
^ Venue    |  TBA  |   |   TBA   |     |

<html><font color="red"> <b>The Golden Rule of CSCI5510: </b></font></html> No member of the CSCI5510 community shall take unfair advantage of any other member of the CSCI5510 community.

====== Course Description ======
This course aims at teaching students the state-of-the-art big data analytics, including techniques, software, applications, and perspectives with massive data.  The class will cover, but not be limited to, the following topics: distributed file systems such as Google File System, Hadoop Distributed File System, CloudStore, and map-reduce technology; similarity search techniques for big data such as minhash, locality-sensitive hashing; specialized processing and algorithms for data streams; big data search and query technology; big graph analysis; recommendation systems for Web applications.  The applications may involve business applications such as online marketing, computational advertising, location-based services, social networks, recommender systems, healthcare services, also covered are scientific and astrophysics applications such as environmental sensor applications, nebula search and query, etc. 

本課程旨在教導學生最先進的針對大數據的分析，包括技術、軟件、應用和遠景。本課程內容將包括，但不限於以下內容：分佈式文件系統如谷歌文件系統，Hadoop文件系統，CloudStore等和Map-reduce技術；大數據的相似搜索技術，如最小哈希，局部敏感哈希等；針對數據流的專門處理方法和算法；大數據的搜索和查詢技術；互聯網應用中的廣告管理和推薦系統。本課涉及的應用程序可能包括商業應用程序，如網絡營銷、計算廣告、基於位置的服務、社交網絡、推薦系統、醫療保健服務和科學及天體物理學領域的應用，如環境傳感器的應用，星雲搜索和查詢等。


===== Learning Objectives =====
  - To understand the current key issues on big data and the associated business/scientific data applications
  - To teach the fundamental techniques and principles in achieving big data analytics with scalability and streaming capability 
  - To interpret business models and scientific computing results 
  - Able to apply software tools for big data analytics


===== Learning Outcomes =====
At the end of the course of studies, students will have acquired the ability to
  - Understand the key issues on big data and the associated applications in intelligent business and scientific computing. 
  - Acquire fundamental enabling techniques and scalable algorithms in big data analytics.
  - Interpret business models and scientific computing paradigms, and apply software tools for big data analytics.
  - Achieve adequate perspectives of big data analytics in marketing, financial services, health services, social networking, astrophysics exploration, and environmental sensor applications, etc.


===== Learning Activities =====
  - Lectures
  - Tutorials
  - Web resources
  - Videos
  - Quizzes
  - Examinations

====== Personnel ======

| ^  Lecturer  ^  Tutor  ^ Tutor ^
^ Name |  [[:home|Irwin King/Michael R. Lyu]]  |    |   |
^ Email |  king/lyu AT cse.cuhk.edu.hk  |   |    |
^ Office   |  Rm 908  |   |   |
^ Telephone   |   3943 8398/8429   |  | |
^ Office Hour(s)   | TBA  |  | |

Note: This class will be taught in<html> <font color="red">English</font></html>. Homework assignments and examinations will be conducted in English.

====== Syllabus ======
The pdf files are created in Acrobat 6.0. Please obtain the correct version of the [[http://www.adobe.com/prodindex/acrobat/readstep.html#reader | Acrobat Reader]] from Adobe.

^  Week  ^  Date  ^  Topics  ^  Tutorials  ^  Homework & Events  ^  Resources   ^   
|  1  |  2/9  |  Introduction and Motivation \\ \\ [[|01-Introduction.pdf]]  | | |  [[http://infolab.stanford.edu/~ullman/mmds/ch1.pdf|Ch. 1 of MMDS]]  |
|  2  |  9/9  |  MapReduce\\ \\ [[|02-MapReduce.pdf]]  |  \\ \\   |  \\ \\  |   [[http://infolab.stanford.edu/~ullman/mmds/ch2.pdf|Ch. 2 of MMDS]]  \\   [[http://infolab.stanford.edu/~ullman/mmds/ch6.pdf|Ch. 6 of MMDS]]  |
|  3  |  16/9  |  Locality Sensitive Hashing\\ \\ [[|03-lsh.pdf]]       |  \\ \\    |    |   [[http://infolab.stanford.edu/~ullman/mmds/ch3.pdf|Ch. 3 of MMDS]]  |
|  4  |  23/9  |  Mining Data Streams\\ \\  [[|04-stream.pdf]]  |    |    |  [[http://infolab.stanford.edu/~ullman/mmds/ch4.pdf|Ch. 4 of MMDS]]   |
|  5  |  30/9  |  Scalable Clustering  \\ \\ [[|05-clustering.pdf]]    |    |    |   [[http://infolab.stanford.edu/~ullman/mmds/ch7.pdf|Ch. 7 of MMDS]]  |
|  6  |  7/10  |  Dimensionality Reduction \\ \\ [[|06-DR.pdf]]  |    |     |  [[http://infolab.stanford.edu/~ullman/mmds/ch11.pdf|Ch. 11 of MMDS]]  |
|  7  |  14/10  |  Public Holiday  |  Public Holiday  |    |   |
|  8  |  21/10  |  Recommender systems/Matrix Factorization \\ \\  [[|07-mf.pdf]]  |    |    |  [[http://infolab.stanford.edu/~ullman/mmds/ch9.pdf|Ch. 9 of MMDS]]  |
|  9  |  28/10  |  Massive Link Analysis \\ \\  [[|08-link.pdf]]  |   |      |   [[http://infolab.stanford.edu/~ullman/mmds/ch5.pdf|Ch. 5 of MMDS]]  |
|  10  |  4/11  |  Mid-term   | | |    |
|  11  |  11/11  |  Analysis of Massive Graph   \\    \\  [[|09-graph.pdf]]  | | |  [[http://infolab.stanford.edu/~ullman/mmds/ch10.pdf|Ch. 10 of MMDS]]  |
|  12  |  18/11  |  Large Scale SVM\\ \\  [[|10-svm.pdf]]  |    |  \\   |  [[http://www.svms.org/tutorials/Burges1998.pdf|SVM tutorial]]  |
|  13  |  25/11  |  Online Learning \\  \\ [[|11-ol.pdf]]   |    | | [[http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf|Online learning survey]]  |


====== Class Project ======
===== Class Project Presentation Schedule =====
  * TBA

===== Class Project Presentation Requirements=====

====== Examination Matters ======


===== Examination Schedule =====
|              ^        Time           ^        Venue          ^    Notes    ^
^    Midterm Examination\\ Written    |    TBA  |   TBA   |    TBA    |
^    Midterm Examination\\ Programming    |    TBA   |   TBA   |    TBA    |
^    Final Examination    |   TBA | TBA | TBA   |


  * [[http://rgsntl.rgs.cuhk.edu.hk/rws_prd_life/main1.asp|CUHK Registration and Examination]]



===== Written Midterm Matters =====
  - The midterm will test your knowledge of the materials.
  - Answer all questions using the answer booklet.  There will be more available at the venue if needed.
  - Write legibly.  Anything we cannot decipher will be considered incorrect.
  - One A4-sized cheat-sheet page.

====== Grade Assessment Scheme ======
^  Homework\\ Assignments  ^  Project Report  ^  Project Presentation  ^  One-hour  Examination  ^
|  30%  |  25%  |  25%  |  20%  |

  -Assignments (30%) 
    -Written assignments
    -Coding
  -One-hour Examination (20%) 
  - Project (50%)
    - Report (25%)
    - Presentations (25%)
  -Extra Credit (There is no penalty for not doing the extra credit problems. Extra credit will only help you in borderline cases.)

====== Required Background ======
  - Pre-requisites
    - 

====== Reference Books ======


====== FAQ ======

  - **Q: What is departmental guideline for plagiarism?**\\ A: If a student is found plagiarizing, his/her case will be reported to the Department Discipline Committee. If the case is proven after deliberation, the student will automatically fail the course in which he/she committed plagiarism. The definition of plagiarism includes copying of the whole or parts of written assignments, programming exercises, reports, quiz papers, mid-term examinations. The penalty will apply to both the one who copies the work and the one whose work is being copied, unless the latter can prove his/her work has been copied unwittingly. Furthermore, inclusion of others' works or results without citation in assignments and reports is also regarded as plagiarism with similar penalty to the offender. A student caught plagiarizing during tests or examinations will be reported to the Faculty Office and appropriate disciplinary authorities for further action, in addition to failing the course.

====== Resources ======

  -[[http://pajek.imfm.si/doku.php|Pajek, a network analysis and visualization program.]]
  -[[http://vlado.fmf.uni-lj.si/pub/networks/data/default.htm|Package for Large Network Analysis]]
  -[[http://www.analytictech.com/downloaduc6.htm|UCINET 6]]
  -[[http://www.analytictech.com/Netdraw/netdraw.htm|Netdraw]]
  -[[http://stat.gamma.rug.nl/stocnet/|StOCNET]]

===== Big Data Analytics =====
  * http://infolab.stanford.edu/~ullman/mmds.html \\
  * http://cs246.stanford.edu/ \\

===== Graph Mining =====
  * http://www.cs.cmu.edu/~deepay/mywww/papers/csur06.pdf \\
  * http://cs.stanford.edu/people/jure/talks/www08tutorial/ \\
  * http://www.xifengyan.net/tutorial/KDD08_graph_partI.pdf \\
  * http://www.xifengyan.net/tutorial/KDD08_graph_partII.pdf

===== Link Analysis===== 
  * http://analytics.ijs.si/events/Tutorial-TextMiningLinkAnalysis-KDD2007-SanJose-Aug2007/ \\
  * http://www.sigkdd.org/explorations/issues/7-2-2005-12/1-Getoor.pdf \\
  * http://www.ncjrs.gov/pdffiles1/nij/grants/219552.pdf \\
  * http://delab.csd.auth.gr/~dimitris/papers/ENVO07LARskm.pdf

===== Learning to Rank===== 
  * http://www2009.org/pdf/T7A-LEARNING%20TO%20RANK%20TUTORIAL.pdf\\
  * http://radlinski.org/papers/LearningToRank_NESCAI08.pdf\\
  * http://www.aclweb.org/anthology/P/P09/P09-5005.pdf\\
  * http://www.cse.iitb.ac.in/~soumen/doc/www2007/TutorialSlides.pdf

===== Recommender Systems===== 
  * http://en.wikipedia.org/wiki/Recommender_system
  * http://www.deitel.com/ResourceCenters/Web20/RecommenderSystems/RecommenderSystemsTutorialsandWebcasts/tabid/1313/Default.aspx
  * http://www.computer.org/portal/web/csdl/doi/10.1109/TKDE.2005.99
  * http://www.springerlink.com/content/n881136032u8k111/
  * http://www.csd.abdn.ac.uk/~jmasthof/Publications/WPRSIUI07.pdf



===== Human Computation/Social Games  =====
  * http://www.gwap.com/gwap/ \\
  * http://www.cs.cmu.edu/~biglou/ \\

===== Opinion Mining/Sentiment Analysis  =====
  * http://www.cs.uic.edu/~liub/FBS/opinion-mining-sentiment-analysis.pdf \\
  * http://www.cs.cornell.edu/home/llee/omsa/omsa-published.pdf \\
  * http://www.cs.cmu.edu/~wcohen/10-802/sentiment-sep-4.ppt \\

===== Visualization =====
  -[[http://manyeyes.alphaworks.ibm.com/manyeyes/|Many Eyes Visualization]]

===== Programming =====
  -[[http://networkx.lanl.gov/|NetworkX, a Python package for complex networks]]
  -[[http://www.wolfram.com/|Mathematica from Wolfram]]
    -[[http://demonstrations.wolfram.com/|Wolfram Demonstrations]]

===== Midterm Evaluation Sign-up Sheet =====
  * The time slots are for Thursday, November 8, 2012.
  * The venue is in HSH Room 1022 (seminar room).
  * Please enter all team members' name in the slot in either the A.M. or the P.M. table.
  * Instructions
    - Put the name of all your team members under the "Real name" column.
    - Select at least one slot from either the A.M. or P.M. table.
    - Press "Submit".
    - Make sure it does not conflict with others.

Midterm Project Evaluation PM
<doodle>
^ 9:30 ^ 9:45 ^ 10:00 ^ 10:15 ^ 10:30 ^ 10:45 ^ 11:00 ^ 11:15 ^ 11:30 ^ 11:45 ^
</doodle>

Midterm Project Evaluation PM
<doodle >
^ 12:30 ^ 12:45 ^ 1:00 ^ 1:15 ^ 1:30 ^ 1:45 ^ 2:00 ^ 2:15 ^ 2:30 ^ 2:45 ^ 3:00 ^
</doodle>

===== Final Project Presentation Sign-up Sheet =====
  * The time slots are for Tuesday, December 4, 2012.
  * The venue is in KKB 101 (classroom).
  * Please enter all team members' name in the slot in either the Session 1 or the Session 2 table.
  * Instructions
    - Put the name of all your team members under the "Real name" column.
    - Select at least one slot from either the Session 1 or Session 2 table.
    - Press "Submit".
    - Make sure it does not conflict with others.

Final Project Presentation Session 1
<doodle>
^ 9:00 ^ 9:15 ^ 9:30 ^ 9:45 ^ 10:00 ^ 10:15 ^ 10:30 ^ 10:45 ^ 11:00 ^ 
</doodle>

Final Project Presentation Session 2
<doodle>
^ 11:15 ^ 11:30 ^ 11:45 ^ 12:00 ^ 12:15 ^ 12:30 ^ 12:45 ^ 13:00 ^ 13:15 ^ 13:30 ^ 
</doodle>
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