CSC7130 Advanced Artificial Intelligence

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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
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

  1. Lectures
  2. Web resources
  3. Videos
  4. Examinations


Lecturer Tutor
Name Irwin King Tom Chao Zhou
Email king AT czhou AT
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.


The pdf files are created in Acrobat 6.0. Please obtain the correct version of the 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
[ pdf ]
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

[ pdf ]
Sample Solutions

[ pdf ]

Grades of the Class below.

Examination Matters

Examination Schedule

Time Venue Notes
Midterm Examination
Midterm Examination
Final Examination TBD TBD The final examination covers all materials presented in the class but emphasizes more on the materials after the midterm.

Written Examination Matters

  • TBA

Grade Assessment Scheme

  • TBA

Required Background

Reference Books

  1. [2009, book | www]
    Simon Haykin, Neural Networks and Learning Machines, 3rd ed., Pearson International Edition, 2009.
  2. [2009, book | www]
    Xiaojin Zhu, Andrew B. Goldberg, Ronald Brachman, and Thomas Dietterich, Introduction to Semi-Supervised Learning, Morgan and Claypool Publishers, 2009.


  1. 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.


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