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):
See also Unit timetable information
CSE5610
FIT9131 or FIT5131 or FIT9017 or equivalent
Fundamental math with introductory knowledge of probability
David Dowe
Consultation hours: To Be Announced
To Be Announced
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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 to Artificial Intelligence | Assessment task 3: Tutorial Work is assessed each week during and after each tutorial session |
| 2 | Problem Solving | |
| 3 | Knowledge Representation (Logic) | |
| 4 | Planning | |
| 5 | Soft Computing & Probability | |
| 6 | Stochastic Search & Evolutionary Algorithms | |
| 7 | Bayesian Networks | |
| 8 | Intelligent Decision Support | Assignment 1: Assignment 1 - Problem Solving, Knowledge Representation and Bayesian Networks due |
| 9 | Supervised Machine Learning | |
| 10 | Unsupervised Machine Learning | |
| 11 | Stochastic Problem Solving | Assignment 2: Machine Learning due |
| 12 | Agent-Based Modelling | |
| 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, Knowledge Representation and Bayesian Networks | 10% | Week 8 |
| Assignment 2 - Machine Learning | 10% | Week 11 |
| Assessment Task 3: Tutorial Participation | 10% | After each tutorial session |
| 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 submitted answers to problems and/or Bayesian networks.
The criteria used to assess are:
Degree of correctness and level of understanding of the underlying modelling techniques and any other relevant material.
Degree of completeness of your answers to tutorial questions and any other relevant material.
The book Wallace, C. S. (2005), ``Statistical and Inductive Inference by Minimum Message Length'', Springer, ISBN: 0-387-23795-X, 432pp is an advanced text on the machine learning technique called Minimum Message Length (MML). It is available both in printed hard copy and in electronic form from the library.
Monash Library Unit Reading List (if applicable to the unit)
http://readinglists.lib.monash.edu/index.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 at http://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 electronic submission). 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.
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: www.policy.monash.edu.au/policy-bank/academic/education/index.html
Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/
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