This unit introduces the main problems and approaches to designing intelligent software systems including automated search methods, knowledge representation and reasoning, planning, reasoning under uncertainty, machine learning paradigms, and evolutionary algorithms.
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
(b.) Additional requirements (all students):
CSE5610
FIT5131 or FIT9017 or equivalent
Fundamental math with introductory knowledge of probability
Mark Carman
To be confirmed
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Assessment weighting has been changed due to students' feedback.
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| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Introduction | |
| 2 | Problem Solving | |
| 3 | Knowledge Representation (Logic) | |
| 4 | Planning | Assignment 1 due 22 August 2014 |
| 5 | Soft Computing & Probability | |
| 6 | Stochastic Search & Evolutionary Algorithms | |
| 7 | Bayesian Networks | |
| 8 | Intelligent Decision Support | Assignment 2 due 19 September 2014 |
| 9 | Supervised Machine Learning | |
| 10 | Unsupervised Machine Learning | |
| 11 | Stochastic Problem Solving | |
| 12 | Agent-Based Modeling | Assignment 3 due 24 October 2014 |
| SWOT VAC | No formal assessment is undertaken in 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 learning system.
Examination (3 hours): 70%; In-semester assessment: 30%
| Assessment Task | Value | Due Date |
|---|---|---|
| Assignment 1 - Problem Solving and Knowledge Representation | 10% | 22 August 2014 |
| Assignment 2 - Bayesian Networks and Soft Computing | 10% | 19 September 2014 |
| Assignment 3 - Machine Learning | 10% | 24 October 2014 |
| Examination 1 | 70% | To be advised |
Faculty Policy - Unit Assessment Hurdles (http://intranet.monash.edu.au/infotech/resources/staff/edgov/policies/assessment-examinations/assessment-hurdles.html)
Academic Integrity - Please see resources and tutorials at http://www.monash.edu/library/skills/resources/tutorials/academic-integrity/
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.
Monash Library Unit Reading List (if applicable to the unit)
http://readinglists.lib.monash.edu/index.html
Faculty of Information Technology Style Guide
Examination/other end-of-semester assessment feedback may take the form of feedback classes, provision of sample answers or other group feedback after official results have been published. Please check with your lecturer on the feedback provided and take advantage of this prior to requesting individual consultations with staff. If your unit has an examination, you may request to view your examination script booklet, see http://intranet.monash.edu.au/infotech/resources/students/procedures/request-to-view-exam-scripts.html
Types of feedback you can expect to receive in this unit are:
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.monash.edu.au/exams/special-consideration.html
No resubmissions.
See Library Guides for Citing and Referencing athttp://guides.lib.monash.edu/content.php?pid=88267&sid=656564
It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/student-academic-integrity-managing-plagiarism-collusion-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). Please note that it is your responsibility to retain copies of your assessments.
If Electronic Submission has been approved for your unit, please submit your work via the learning system for this unit, which you can access via links in the my.monash portal.
Please check with your lecturer before purchasing any Required Resources. Limited copies of 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
Limited copies of prescribed texts are available for you to borrow in the library.
Russell, S and Norvig, P. (2010). Artificial Intelligence - A Modern Approach. (3rd Edition) Prentice-Hall.
Witten, I and Frank, E. (2005). Data Mining - Practical Machine Learning Tools and Techniques. (3rd Edition) Elsevier.
Korb, K and Nicholson, A. (2011). Bayesian Artificial Intelligence. (2nd Edition) CRC Press.
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Key educational policies include:
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