Faculty of Informatics
CSCI964 Neural Computing
Subject Outline
Autumn Session 2007
Head of
School –Professor Philip Ogunbona, Student Resource Centre, Tel: (02) 4221 3606
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Professor
John Fulcher |
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Telephone
Number: |
4221 3811 |
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Email: |
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Location: |
3.223 |
Professor Fulcher’s
Consultation Times During Session
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Day |
Time |
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Mondays |
1330-1530 |
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Wednesdays |
0930-1130 |
Subject
Organisation
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Session: |
Autumn
Session, |
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Credit
Points |
6 |
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Contact
hours per week: |
2 hour
lectures |
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Lecture
Times & Location: |
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Tutorial
Day, Time and Location can be found at: |
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Students
should check the subject’s web site regularly as important information,
including details of unavoidable changes in assessment requirements will be
posted from time to time. Any information
posted to the web site is deemed to have been notified to all students.
On successful completion of this subject, students should be
able to: i) explain the architecture and learning algorithms of the more
commonly encountered neural network models; ii) understand the strengths and
limitations of artificial neural networks (ANNs); iii) be able to apply ANNs to
typical pattern recognition and/or classification problems; iv) understand the
need for preprocessing the available neural data.
Attendance
Requirements
It is the responsibility
of students to attend all lectures/tutorials/labs/seminars/practical work for
subjects for which you are enrolled.
It should be noted that according to Course Rule 003{Interpretation Point 2 (t)} each credit point for a single session subject has the value of about two hours per week including class attendance. Therefore, the amount of time spent on each 6 credit point subject should be at least 12 hours per week, which includes lectures/tutorials/labs etc
The
presentation consists of lectures. The
lectures will introduce and discuss theoretical concepts, as well as, introduce
the software applications that the students are required to use for the
completion of their assignments. There
are not any scheduled laboratory classes, but it is expected that students will
use the School’s computer laboratories in their own time to complete the
assignments.
Subject
Materials
§
Haykin, Neural
Networks: a Comprehensive Foundation (2nd ed), Maxwell/Macmillan
1999.
This subject has the
following assessment components
These readings/references are recommended only and are not intended to be an exhaustive list. Students are encouraged to use the library catalogue and databases to locate additional readings
This subject has the
following assessment components.
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Assessment Items & Format |
Percentage
of Final Mark |
Due
Date |
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Five
Assignments |
40% (8% each) |
Weeks
4,6,8,10,12 (hard
copy submission during lecture timeslot) |
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Examination |
60% |
During Exam Period |
·
Assignment submissions dates and instructions
will be provided on the assignment specifications;
·
If submission dates are altered, students will
be notified in lectures or on the subject website;
·
Assignments will be returned to students during
lectures or from your lecturer during consultation times;
·
Penalties will apply to work submitted late,
except if special consideration or an extension has been granted by your
subject coordinator or lecturer. 10% will be deducted for each day
overdue. Any submission submitted more
than 5 days after the due date may score 0 (zero) mark;
Students must refer to the Faculty Handbook or online references which
contains a range of policies on educational issues and student matters.
Please note that if this is your last session and you are granted a supplementary exam, be aware that your results will not be processed in time to meet the graduation deadline.
Plagiarism
When you submit an assessment task, you are
declaring the following
1.
It
is your own work and you did not collaborate with or copy from others.
2.
You
have read and understand your responsibilities under the
3.
You
have not plagiarised from published work (including the internet). Where you
have used the work from others, you have referenced it in the text and provided
a reference list at the end ot the assignment.
4.
Plagiarism
will not be tolerated.
5.
Students
are responsible for submitting original work for assessment, without
plagiarising or cheating, abiding by the University’s policies on Plagiarism as
set out in the Calendar under University Policies, and in Faculty handbooks and
subject guides. Plagiarism has led to the expulsion from the University.
This outline should be read in conjunction with the following documents:
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Code of
Practice - Teaching and Assessment http://www.uow.edu.au/handbook/codesofprac/teaching_code.html |
Key Dates |
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Code of
Practice - Students http://www.uow.edu.au/handbook/codesofprac/cop_students.html |
Information
Literacies Introduction Program |
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Acknowledgement
Practice Plagiarism will not be
tolerated |
Student
Academic Grievance Policy http://www.uow.edu.au/handbook/codesofprac/cop_supervision.html#8 |
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Special
Consideration Policy http://www.uow.edu.au/handbook/courserules/specialconsideration.html |
Code of
Practice-Honours |
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Non-Discriminatory
Language Practice and Presentation |
Intellectual
Property Policy http://www.uow.edu.au/research/researchmanagement/1998IP.html |
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Occupational Health and Safety http://staff.uow.edu.au/ohs/commitment/OHS039-ohspolicy.pdf |
SCSSE
Internet Access & Student Resource Centre http://www.sitacs.uow.edu.au/info/current/internet_access_and_resource.shtml |
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SCSSE
Computer Usage Rules http://www.itacs.uow.edu.au/info/current/support/labs/rules.shtml |
SCSSE Style
Guide for Footnotes, Documentation, Essay and Report Writing |
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SCSSE
Student Guide |
Informatics
Faculty Librarian, Ms
Annette Meldrum, phone: 4221 4637,ameldrum@uow.edu.au |
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SCSSE
Subject Outlines |
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