Intelligent software systems are becoming increasingly relevant in real-world applications. The subject studies basic Artificial Intelligence techniques that facilitate smart applications in the domains of Pervasive Computing, Web Services and Business Intelligence. The subject will examine in-depth fundamental Artificial Intelligence techniques, as well as the current software technologies used in the construction on intelligent systems.
Objectives
At the completion of this subject, the students will have:
Knowledge of: 1. The applications of intelligent software systems in the domains of Pervasive Computing, Web Services and Business Intelligence 2. The principles and theoretical underpinning of intelligent software systems.
Understanding of: 3. Models and approaches to building intelligent software systems 4. Different software toolkits and development environments 6. Current research trends in the field
Skills to: 8. Design and develop of intelligent applications particularly in the domains of Pervasive Computing, Web Services and Business Intelligence 9. Select and apply appropriate tools for a particular application
Attitudes: 10. The subject is designed to foster critical and independent analysis of how intelligent techniques can be used to enhance software applications and the development of smart environments.
Prerequisites
No prerequisites
Unit relationships
You may not study this unit and CSE3309 in your degree or previous Monash degrees.
Texts and software
Required text(s)
Part I: Artificial Intelligence: A Modern Approach, Russell and Norvig, Prentice Hall.
Textbook availability
All resources including publications related to the subject can be downloaded from the subject resource web site.
Software requirements
There is no software requirement.
Hardware requirements
Students are required to have access to the standard system configuration available in the computer labs, and regular Internet access.
Recommended reading
Russell and Norvig: Artificial Intelligence -- A Modern Approach, Prentice Hall, second edition.
Korb and Nicholson: Bayesian Artificial Intelligence, Capman and Hall.
Library access
You may need to access the Monash library either personally to be able to satisfactorily complete the subject. Be sure to obtain a copy of the Library Guide, and if necessary, the instructions for remote access from the library website.
Study resources
Study resources for CSE5610 are:
Lecture notes provided on MUSO and tutorial questions.
Structure and organisation
Week
Topics
1
Introduction to AI, Problem solving as search
2
Problem Solving as search, Knowledge representation
3
Knowledge representation
4
Planning
5
Probability and Bayesian networks
6
Machine learning
Non teaching week
7
TBA
8
TBA
9
TBA
10
TBA
11
TBA
12
TBA
13
TBA
Timetable
The timetable for on-campus classes for this unit can be viewed in Allocate+
Assessment
Assessment weighting
Assessment for the unit consists of
Part I: 2 assignments with a weighting of 10%
Part II: 3 assignments with a weighting of 20%
A 2 hour examination with a weighting of 70%. Read this section VERY carefully.
Assessment Policy
To pass this unit you must:
Obtain an overall mark of 50 or more.
Your score for the unit will be calculated by:
0.3A + 0.7E
where
A -- assignments E -- final exam (2 hours)
Assessment Requirements
Assessment
Due Date
Weighting
Problem solving as search and knowledge representation
4/4/07
10%
Planning and Bayesian networks
18/4/07
20 %
Examination is 2 hours and is closed book
Exam period (S1/07) starts on 07/06/07
70 %
Assignment specifications will be made available On the CSE5610 MUSO site..
Assignment Submission
Assignments will be submitted by paper submission to the lecturer. Students should submit the assignments with the appropriate cover sheet correctly filled out and attached. Do not email submissions.
Extensions and late submissions
Late submission of assignments
Assignments received after the due date will be subject to a penalty of 10% deduction in marks per day, Assignments received later than one week after the due date will not normally be accepted.
This policy is strict because comments or guidance will be given on assignments as they are returned, and sample solutions may also be published and distributed, after assignment marking or with the returned assignment.
Extensions
It is your responsibility to structure your study program around assignment deadlines, family, work and other commitments. Factors such as normal work pressures, vacations, etc. are seldom regarded as appropriate reasons for granting extensions.
Requests for extensions must be made by email to the unit lecturer at least two days before the due date. You will be asked to forward original medical certificates in cases of illness, and may be asked to provide other forms of documentation where necessary. A copy of the email or other written communication of an extension must be attached to the assignment submission.
Grading of assessment
Assignments, and the unit, will be marked and allocated a grade according to the following scale:
Grade
Percentage/description
HD High Distinction -
very high levels of achievement, demonstrated knowledge and understanding, skills in application and high standards of work encompassing all aspects of the tasks.
In the 80+% range of marks for the assignment.
D Distinction -
high levels of achievement, but not of the same standards. May have a weakness in one particular aspect, or overall standards may not be quite as high.
In the 70-79% range.
C Credit -
sound pass displaying good knowledge or application skills, but some weaknesses in the quality, range or demonstration of understanding.
In the 60-69% range.
P Pass
acceptable standard, showing an adequate basic knowledge, understanding or skills, but with definite limitations on the extent of such understanding or application. Some parts may be incomplete.
In the 50-59% range.
N Not satisfactory
failure to meet the basic requirements of the assessment.
Below 50%.
Assignment return
We will aim to have assignment results made available to you within two weeks after assignment receipt.
Feedback
Feedback to you
You will receive feedback on your work and progress in this unit. This feedback may be provided through your participation in tutorials and class discussions, as well as through your assignment submissions. It may come in the form of individual advice, marks and comments, or it may be provided as comment or reflection targeted at the group. It may be provided through personal interactions, such as interviews and on-line forums, or through other mechanisms such as on-line self-tests and publication of grade distributions.
Feedback from you
You will be asked to provide feedback to the Faculty through a Unit Evaluation survey at the end of the semester. You may also be asked to complete surveys to help teaching staff improve the unit and unit delivery. Your input to such surveys is very important to the faculty and the teaching staff in maintaining relevant and high quality learning experiences for our students.
And if you are having problems
It is essential that you take action immediately if you realise that you have a problem with your study. The semester is short, so we can help you best if you let us know as soon as problems arise. Regardless of whether the problem is related directly to your progress in the unit, if it is likely to interfere with your progress you should discuss it with your lecturer or a Community Service counsellor as soon as possible.
Unit improvements
The material in the first half of the unit has been changed to foundations of Artificial Intelligence, in order to provide students with a solid foundation on which to base the applications discussed in the second half.
Plagiarism and cheating
Plagiarism and cheating are regarded as very serious offences. In cases where cheating has been confirmed, students have been severely penalised, from losing all marks for an assignment, to facing disciplinary action at the Faculty level. While we would wish that all our students adhere to sound ethical conduct and honesty, I will ask you to acquaint yourself with Student Rights and Responsibilities and the Faculty regulations that apply to students detected cheating as these will be applied in all detected cases.
In this University, cheating means seeking to obtain an unfair advantage in any examination or any other written or practical work to be submitted or completed by a student for assessment. It includes the use, or attempted use, of any means to gain an unfair advantage for any assessable work in the unit, where the means is contrary to the instructions for such work.
When you submit an individual assessment item, such as a program, a report, an essay, assignment or other piece of work, under your name you are understood to be stating that this is your own work. If a submission is identical with, or similar to, someone else's work, an assumption of cheating may arise. If you are planning on working with another student, it is acceptable to undertake research together, and discuss problems, but it is not acceptable to jointly develop or share solutions unless this is specified by your lecturer.
Intentionally providing students with your solutions to assignments is classified as "assisting to cheat" and students who do this may be subject to disciplinary action. You should take reasonable care that your solution is not accidentally or deliberately obtained by other students. For example, do not leave copies of your work in progress on the hard drives of shared computers, and do not show your work to other students. If you believe this may have happened, please be sure to contact your lecturer as soon as possible.
Cheating also includes taking into an examination any material contrary to the regulations, including any bilingual dictionary, whether or not with the intention of using it to obtain an advantage.
Plagiarism involves the false representation of another person's ideas, or findings, as your own by either copying material or paraphrasing without citing sources. It is both professional and ethical to reference clearly the ideas and information that you have used from another writer. If the source is not identified, then you have plagiarised work of the other author. Plagiarism is a form of dishonesty that is insulting to the reader and grossly unfair to your student colleagues.
Communication
Communication methods
Through tutors, by email or to lecturer before or after lectures.
Notices
Notices related to the unit during the semester will be placed on MUSO Announcements in the Unit Website. Check this regularly. Failure to read the Announcements is not regarded as grounds for special consideration.
Consultation Times
As advertised in the lecturer's web site and by appointment.
If direct communication with your unit adviser/lecturer or tutor outside of consultation periods is needed you may contact the lecturer and/or tutors at:
Professor Ingrid Zukerman Professor Phone +61 3 990 55202 Fax +61 3 990 55157
Mr Simon Cuce
All email communication to you from your lecturer will occur through your Monash student email address. Please ensure that you read it regularly, or forward your email to your main address. Also check that your contact information registered with the University is up to date in My.Monash.
