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
Consultation hours: Tuesday 2-3 (Clayton 63/205), Thursday 3-4 (Caulfield, H7.34)
Owen Woodberry
Consultation hours: TBD
Examination (3 hours): 70%; In-semester assessment: 30%
| Assessment Task | Value | Due Date |
|---|---|---|
| Assignment 1 - Knowledge Representation and Planning | 10% | 23 March 2012 |
| Assignment 2 - Bayesian Networks and Soft Computing | 10% | 27 April 2012 |
| Assignment 3 - Machine Learning | 10% | 25 May 2012 |
| 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
Please check with your lecturer before purchasing any Required Resources. Prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.
Netica (free)
Netlogo (free)
Weka Data Mining Toolkit (free)
Web access
Prescribed texts are available for you to borrow in the library.
Russell, S. and Norvig, P.. (2010). Artificial Intelligence -- A Modern Approach. (3rd) Prentice-Hall.
Witten, I and Frank, E. (2005). Data Mining. (3rd) Elsevier.
Korb, K and Nicholson, A.. (2010). Bayesian Artificial Intelligence. (2nd) CRC.
| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Introduction | |
| 2 | Problem Solving | |
| 3 | Knowledge Representation | |
| 4 | Planning | Assignment 1 due 23 March 2012 |
| 5 | Soft Computing | |
| 6 | Evolutionary Algorithms | |
| 7 | Bayesian Networks | |
| 8 | Intelligent Decision Support | Assignment 2 due 27 April 2012 |
| 9 | Supervised Machine Learning | |
| 10 | Unsupervised Machine Learning | |
| 11 | Agent-Based Modeling | |
| 12 | Stochastic Problem Solving | Assignment 3 due 25 May 2012 |
| SWOT VAC | No formal assessment is undertaken SWOT VAC | |
| Examination period | LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/ academic/education/assessment/ assessment-in-coursework-policy.html |
*Unit Schedule details will be maintained and communicated to you via your MUSO (Blackboard or Moodle) learning system.
Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)
Correctness and completeness of answers to problems.
Correctness and completeness of submitted answers and/or Bayesian networks.
Correctness and completeness of answers to machine learning problems.
It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/plagiarism-procedures.html) for students to submit an assignment coversheet for each assessment item. Faculty Assignment coversheets can be found at http://www.infotech.monash.edu.au/resources/student/forms/. Please check with your Lecturer on the submission method for your assignment coversheet (e.g. attach a file to the online assignment submission, hand-in a hard copy, or use an online quiz).
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.
Students can expect assignments to be returned within two weeks of the submission date or after receipt, whichever is later.
No resubmissions.
See Libary Guides for Citing and Referencing athttp://guides.lib.monash.edu/content.php?pid=88267&sid=656564
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. For Sunway see http://www.monash.edu.my/Student-services, and for South Africa see http://www.monash.ac.za/current/
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. At Sunway, visit the Library and Learning Commons at http://www.lib.monash.edu.my/. At South Africa visit http://www.lib.monash.ac.za/.
Academic support services may be available for students who have a disability or medical condition. Registration with the Disability Liaison Unit is required. Further information is available as follows:
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.