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.
Reza Haffari
David Dowe
| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Unit introduction, Introduction to Machine Learning | |
| 2 | Non-parametric Methods | |
| 3 | Linear Models for Regression | |
| 4 | Linear Models for Classification | |
| 5 | Graphical Models | |
| 6 | K-means, Mixture of Gaussians, Expectation Maximization | Assignment 1 due Week 8 |
| 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 2 due Week 11, Thursday, 17 October 2013 |
| 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 learning system.
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 - Machine Learning: Supervised & Unsupervised Models | 30% | Week 8 |
| Assignment 2 - MML modelling | 30% | Week 11, Thursday, 17 October 2013 |
| Examination 1 | 40% | To be advised |
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/
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.
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)
Monash Library Unit Reading List
http://readinglists.lib.monash.edu/index.html
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
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). 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.
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.
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Key educational policies include:
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