Methods from Artificial Intelligence (AI) form the basis for many advanced information systems. These techniques address problems that are difficult to solve or not efficiently solvable with conventional techniques. Building on the undergraduate curriculum this unit introduces the student to advanced AI methods and their applications in information systems.
2 hrs lectures/wk
For on-campus students, workload commitments are: (12 hours per week total)
Completion of the Bachelor of Computer Science or equivalent to the entry requirements for the Honours program. Students must also have enrolment approval from the Honours Coordinator.
Gholamreza Haffari
David Dowe
Assignment and Examination, relative weight depending on topic composition. When no exam is given students will be expected to demonstrate their knowledge by solving practical problems and maybe required to give an oral report.
| Assessment Task | Value | Due Date |
|---|---|---|
| Assignment 1 - Supervised Learning | 15% | Week 4 |
| Assignment 2 - Parametric Methods, Clustering | 15% | Week 6 |
| Assignment 3 - MML modelling | 30% | Week 11, Thursday, 11 October 2012 |
| Examination 1 | 40% | 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:
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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
Limited copies of prescribed texts are available for you to borrow in the library.
C. S. Wallace. (2005). Statistical and Inductive Inference by Minimum Message Length. () Springer (ISBN: 0-387-23795-X).
Ethem ALPAYDIN. (2010). Introduction to Machine Learning. () The MIT Press.
| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Unit introduction, Introduction to Machine Learning | |
| 2 | Supervised Learning (PAC theory, ...) | Assignment 1 released Week 2 |
| 3 | Parametric Methods (maximum likelihood, bias-variance, ...) | |
| 4 | Clustering (mixture models, k-means, ..) | Assignment 1 due Week 4; Assignment 2 released Week 4 |
| 5 | non-parametric methods (k-nearest neighbor, ...) | |
| 6 | Decision Trees | Assignment 2 due Week 6 |
| 7 | Bayesianism, Minimum Message Length (MML), inference, prediction | |
| 8 | MML multinomial; MML clustering and mixture modelling | |
| 9 | MML decision trees (and graphs) and log-loss | |
| 10 | Neyman-Scott and related problems for Maximum Likelihood | |
| 11 | MML Bayesian nets, grammatical inference | Assignment 3 due Week 11, Thursday, 11 October 2012 |
| 12 | Algorithmic information theory, formal definitions of intelligence | |
| 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 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)
Academic Integrity - Please see the Demystifying Citing and Referencing tutorial at http://lib.monash.edu/tutorials/citing/
Further details will be provided in the assignment handout.
Quality of answers to questions, demonstrates understanding of the learning material.
Further details will be provided in the assignment handout.
Further details will be provided in the assignment handout.
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.
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You can find Monash's Education Policies at:
http://policy.monash.edu.au/policy-bank/academic/education/index.html
Key educational policies include:
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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:
Additional reading:
Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006.
D. L. Dowe (2011a), "MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness", Handbook of the Philosophy of Science - (HPS Volume 7) Philosophy of Statistics, P.S. Bandyopadhyay and M.R. Forster (eds.), Elsevier, pp901-982, 1/June/2011 (accessible via www.csse.monash.edu.au/~dld/David.Dowe.publications.html#Dowe2011a)