This is the foundation unit for the Intelligent Systems specialisation. It introduces the main problems and approaches to designing intelligent software systems including automated search methods, reasoning under uncertainty, planning, software agents, recommender systems, machine learning paradigms, natural language processing, user modelling and evolutionary algorithms.
2 hrs lectures/wk, 2 hrs laboratories/wk
For on campus students, workload commitments per week are:
Students are expected to work 12 hours per week.
CSE5610
Kevin Korb
Kevin Korb
Contact hours: 2-3 pm Thursdays - email for appointments at other times
At the completion of this unit students will have -
A knowledge and understanding of:
Examination (3 hours): 70%; In-semester assessment: 30%
| Assessment Task | Value | Due Date |
|---|---|---|
| Assignment 1 - Knowledge Representation and Planning | 10% | 25 March 2011 |
| Assignment 2 - Bayesian Networks and Soft Computing | 10% | 21 April 2011 |
| Assignment 3 - Machine Learning | 10% | 27 May 2011 |
| Examination 1 | 70% | To be advised |
Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through SETU, Student Evaluation of Teacher and Unit. The University's student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.
For more information on Monash's educational strategy, and on student evaluations, see:
http://www.monash.edu.au/about/monash-directions/directions.html
http://www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html
If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp
Netica (free)
Weka Data Mining Toolkit (free)
Web access
| Week | Date* | Activities | Assessment |
|---|---|---|---|
| 0 | 21/02/11 | No formal assessment or activities are undertaken in week 0 | |
| 1 | 28/02/11 | Introduction | |
| 2 | 07/03/11 | Problem solving as search | |
| 3 | 14/03/11 | Knowledge representation | |
| 4 | 21/03/11 | Planning | Assignment 1 due 25 March 2011 |
| 5 | 28/03/11 | Natural language processing | |
| 6 | 04/04/11 | Soft computing | |
| 7 | 11/04/11 | Bayesian networks | |
| 8 | 18/04/11 | Intelligent decision support | Assignment 2 due 21 April 2011 |
| Mid semester break | |||
| 9 | 02/05/11 | Supervised machine learning | |
| 10 | 09/05/11 | Unsupervised machine learning | |
| 11 | 16/05/11 | Recommender systems | |
| 12 | 23/05/11 | Artificial Life | Assignment 3 due 27 May 2011 |
| 30/05/11 | SWOT VAC | No formal assessment is undertaken SWOT VAC | |
*Please note that these dates may only apply to Australian campuses of Monash University. Off-shore students need to check the dates with their unit leader.
To pass a unit which includes an examination as part of the assessment a student must obtain:
If a student does not achieve 40% or more in the unit examination or the unit non-examination total assessment, and the total mark for the unit is greater than 50% then a mark of no greater than 49-N will be recorded for the unit
Will be available on Moodle.
Will be available on Moodle.
Will be available on Moodle.
Assignment coversheets are available via
"Student Forms" on the Faculty website: http://www.infotech.monash.edu.au/resources/student/forms/
You MUST submit a completed coversheet with all assignments, ensuring
that the plagiarism declaration section is signed.
Submission must be made by the due date otherwise penalties will be enforced.
You must negotiate any extensions formally with your campus unit leader via the in-semester special consideration process: http://www.infotech.monash.edu.au/resources/student/equity/special-consideration.html.
Monash has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University's academic standards, and to provide advice on how they might uphold them.
You can find Monash's Education Policies at:
http://policy.monash.edu.au/policy-bank/academic/education/index.html
Key educational policies include:
The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at www.monash.edu.au/students The Monash University Library provides a range of services and resources that enable you to save time and be more effective in your learning and research. Go to http://www.lib.monash.edu.au or the library tab in my.monash portal for more information. Students who have a disability or medical condition are welcome to contact the Disability Liaison Unit to discuss academic support services. Disability Liaison Officers (DLOs) visit all Victorian campuses on a regular basis
Reading List
Prescribed text:
Russell, S. and Norvig, P. (2010). Artificial Intelligence -- A Modern Approach, 3rd ed. Prentice Hall.
Recommended texts:
Witten, I and Frank, E. (2005). Data Mining -- Practical Machine Learning Tools and Techniques, 3rd ed. Elsevier.
Korb, K and Nicholson, A. (2010). Bayesian Artificial Intelligence, 2nd ed. CRC Press.