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FIT4009 Advanced topics in intelligent systems - Semester 2, 2013

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.

Mode of Delivery

Clayton (Day)

Contact Hours

2 hrs lectures/wk

Workload requirements

For on-campus students, workload commitments are: (12 hours per week total)

  • Lectures: 2 hours per week
  • Reading, preparation, assignment work, revision: 10 hours per week

Unit Relationships

Prerequisites

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.

Chief Examiner

Campus Lecturer

Clayton

Reza Haffari

David Dowe

Academic Overview

Learning Outcomes

At the completion of this unit students will have:
  • achieved an overview of different technologies that form the basis of intelligent information systems;
  • understood the capabilities of these methods;
  • learned to recognise tasks that can be solved with these methods;
  • the ability to judge the limitations of these methods.With successful completion of the unit the students;
  • the ability to apply the standard techniques in the chosen sub-fields of intelligent information systems to the construction and design of such systems;
  • the ability to critically evaluate the performance of these approaches;
  • the ability to compare these techniques to alternative approaches;
  • gained an appreciation of the practical relevance of intelligent information systems.

Unit Schedule

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.

Assessment Summary

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

Teaching Approach

Problem-based learning
Students are encouraged to take responsibility for organising and directing their learning with support from their lecturers.

Assessment Requirements

Assessment Policy

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/

Assessment Tasks

Participation

  • Assessment task 1
    Title:
    Assignment 1 - Machine Learning: Supervised & Unsupervised Models
    Description:
    This assignment will involve a set of programing questions as well as written questions relating to the learning material.

    Further details will be provided in the assignment handout.
    Weighting:
    30%
    Criteria for assessment:

    Quality of answers to questions, demonstrates understanding of the learning material.

    Further details will be provided in the assignment handout.

    Due date:
    Week 8
  • Assessment task 2
    Title:
    Assignment 2 - MML modelling
    Description:
    This will be a theory and programming assignment.

    Further details will be provided in the assignment handout.
    Weighting:
    30%
    Criteria for assessment:
    • How well solutions are explained.
    • Quality of code demonstrated, where applicable.

    Further details will be provided in the assignment handout.

    Due date:
    Week 11, Thursday, 17 October 2013

Examinations

  • Examination 1
    Weighting:
    40%
    Length:
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:
    Possibly calculators, but nothing else.

Learning resources

Reading list

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

Feedback to you

Types of feedback you can expect to receive in this unit are:
  • Graded assignments without comments
  • Interviews

Extensions and penalties

Returning assignments

Assignment submission

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.

Online submission

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.

Prescribed text(s)

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.

Other Information

Policies

Graduate Attributes Policy

Student services

Monash University Library

Disability Liaison Unit

Students who have a disability or medical condition are welcome to contact the Disability Liaison Unit to discuss academic support services. Disability Liaison Officers (DLOs) visit all Victorian campuses on a regular basis.

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