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====== CSC7130 Advanced Artificial Intelligence ======

==== Breaking News ====
<hi #ffff00>**September 25, 2009**.</hi>  The homework assignment is now available.  Download it from the link below.

===== 2009-10 Term 1 =====


|  ^  Lecture I  ^  Lecture II  ^  Tutorial I  ^  Tutorial II  ^
^ Time    |  Tuesday\\ 7:00 pm - 10:00 pm  |  N/A  |  N/A  |  N/A  |
^ Venue    |  ELB 308  |  N/A  |   N/A   |   N/A   |

<html><font color="red"> <b>The Golden Rule of CSC7130: </b></font></html> No member of the CSC7130 community shall take unfair advantage of any other member of the CSC7130 community.

====== Course Description ======

This course will cover selected topics from: advanced pattern recognition, neural networks, expert systems and fuzzy systems, evolutionary computing, learning theory, constraint processing, logic programming, probabilistic reasoning, computer vision, speech processing, and natural language processing.

===== Learning Objectives =====

===== Learning Outcomes =====


===== Learning Activities =====
  - Lectures
  - Web resources
  - Videos
  - Examinations
====== Personnel ======

| ^  Lecturer  ^  Tutor  ^
^ Name |  [[:home|Irwin King]]  |  [[:people:tom_chao_zhou|Tom Chao Zhou]]  |
^ Email |  king AT cse.cuhk.edu.hk  |  czhou AT cse.cuhk.edu.hk  |
^ Office   |  Rm 908  |   Rm 114/A  |
^ Telephone   |   2609 8398   |  3163 4266  |
^ 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  | 2009/09/08 |  Introduction to Neural Networks and Machine Learning I\\ Brain Theory, Mathematical Abstraction of Neurons  | | |  [ [[http://www.cse.cuhk.edu.hk/~king/PUB/CSC7130-2009.pdf|pdf]] ]\\  <hi #ffff00>NEW!</hi>  |
|  2  | 2009/09/15 |  Introduction to Neural Networks and Machine Learning II\\ Learning Paradigms, Error Correcting Learning, Competitive Learning  | | | |
|  3  | 2009/09/22 |  Introduction to Neural Networks and Machine Learning III\\ Self-organizing Map, Back-propagation Algorithm   | |   Homework Assignment #1\\ \\ [ [[http://www.cse.cuhk.edu.hk/~king/PUB/csc7130hw_2009.pdf|pdf]] ]\\  <hi #ff0000>IMPORTANT!</hi>  |  Sample Solutions\\ \\ [ [[http://www.cse.cuhk.edu.hk/~king/PUB/CSC7130_ANN_sampleanswer.pdf|pdf]] ]\\ \\ \\ Grades of the Class below.\\  {{:teaching:csc7130:csc7130-2009.png?100|Grades}}  |


====== Examination Matters ======
===== Examination Schedule =====
|              ^        Time           ^        Venue          ^    Notes    ^
^    Midterm Examination\\ Written    |    TBD  |   TBD   |    TBD    |
^    Midterm Examination\\ Programming    |    TBD   |   TBD   |    TBD    |
^    Final Examination    |   TBD  |    TBD   |    The final examination covers all materials presented in the class but emphasizes more on the materials after the midterm.   |


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


===== Written Examination Matters =====
  * TBA

====== Grade Assessment Scheme ======

  * TBA


====== Required Background ======

====== Reference Books ======
/*
  - [[http://www.amazon.com/exec/obidos/ASIN/0201498405/qid%3D949475985/sr%3D1-6/002-0319810-4772266|Data Structures and Algorithm Analysis in C]], **Mark Allen Weiss, Addison Wesley, second edition, 1997.**
  - [[http://www.amazon.com/exec/obidos/ASIN/0805316663/ref=sim_books/002-0319810-4772266|Data Structures and Algorithm Analysis in C++]], **Mark Allen Weiss, Addison Wesley, second edition, 1999.**
*/
  - <bibtex>@Book{hayykin2009,
  AUTHOR     = {Haykin, Simon},
  YEAR       = {2009},
  TITLE      = {Neural Networks and Learning Machines},
  PUBLISHER  = {Pearson International Edition},
  address    = {},
  series     = {},
  volume     = {},
  pages      = {},
  month      = {},
  edition    = {3rd},
  keywords   = {},
  URL      = {http://www.amazon.com/Neural-Networks-Learning-Machines-3rd/dp/0131471392/ref=sr_1_1?ie=UTF8&s=books&qid=1252320527&sr=8-1},
  summary    = {}
</bibtex>
  - <bibtex>@Book{zhu2009,
  AUTHOR     = {Zhu, Xiaojin and Goldberg, Andrew B. and Brachman, Ronald and Dietterich, Thomas},
  YEAR       = {2009},
  TITLE      = {Introduction to Semi-Supervised Learning},
  PUBLISHER  = {Morgan and Claypool Publishers},
  address    = {},
  series     = {},
  volume     = {},
  pages      = {},
  month      = {},
  edition    = {},
  keywords   = {},
  URL      = {http://www.amazon.com/Introduction-Semi-Supervised-Synthesis-Artificial-Intelligence/dp/1598295470/ref=sr_1_2?ie=UTF8&s=books&qid=1252320782&sr=1-2},
  summary    = {}
</bibtex>

====== 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 ======
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