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.
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
(b.) Additional requirements (all students):
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
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 the Student Evaluation of Teaching and Units (SETU) survey. 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, see:
www.monash.edu.au/about/monash-directions/ and on student evaluations, see: www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html
Students have appreciated the variety of topics and the introduction to Minimum Message Length (MML) and Machine Learning.
If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp
| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Introduction to Machine Learning | |
| 2 | Non-parametric Methods | |
| 3 | Linear Models for Regression | |
| 4 | Linear Models for Classification | |
| 5 | Graphical Models | Assignment 1 handed out by now |
| 6 | K-means, Mixture of Gaussians, Expectation Maximization | |
| 7 | Bayesianism, Minimum Message Length (MML), inference, prediction | Assignment 1 due Week 7 |
| 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 |
| 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 | 30% | Week 7 |
| Assignment 2 - MML modelling | 30% | Week 11 |
| Examination 1 | 40% | 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/
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). This gives pointers and references to other related work.
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
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.
The Wallace (2005) MML book (some of whose details are at www.csse.monash.edu.au/~dld/CSWallacePublications#MMLBook) is probably available from the library in both hard copy and as .pdf . Please check and confirm.
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.
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
Key educational policies include:
Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/
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 http://www.monash.edu.au/students. For Malaysia 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, resources and programs that enable you to save time and be more effective in your learning and research. Go to www.lib.monash.edu.au or the library tab in my.monash portal for more information. At Malaysia, visit the Library and Learning Commons at http://www.lib.monash.edu.my/. At South Africa visit http://www.lib.monash.ac.za/.