====== CSC7130 Advanced Artificial Intelligence ======
==== Breaking News ====
**September 25, 2009**. 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 |
The Golden Rule of CSC7130: 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 English. 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]] ]\\ NEW! |
| 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]] ]\\ IMPORTANT! | 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.**
*/
- @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 = {}
- @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 = {}
====== 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 ======