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FIT5047 Intelligent systems - Semester 1, 2013

This is the foundation unit for the Intelligent Systems specialisation. It introduces the main problems and approaches to designing intelligent software systems including automated search methods, reasoning under uncertainty, planning, software agents, recommender systems, machine learning paradigms, natural language processing, user modelling and evolutionary algorithms.

Mode of Delivery

Caulfield (Evening)

Contact Hours

2 hrs lectures/wk, 2 hrs laboratories/wk

Workload requirements

For on-campus students, workload commitments per week are:

  • two-hour lecture
  • two-hour lab/tutorial (requiring advance preparation)
  • a minimum of 8 hours of personal study

Students are expected to work 12 hours per week.

Unit Relationships

Prohibitions

CSE5610

Chief Examiner

Campus Lecturer

Caulfield

Mark Carman

Academic Overview

Learning Outcomes

At the completion of this unit students will have -A knowledge and understanding of:
  • the applications of intelligent software systems;
  • the principles and theoretical underpinning of intelligent software systems;
  • models and approaches to building intelligent software systems;
  • different software toolkits and development environments;
  • current research trends in the field.
Developed attitudes that enable them to:
  • foster critical and independent analysis of how intelligent techniques can be used to enhance software applications and the development of smart environments.
Developed the skills to:
  • design and develop of intelligent applications;
  • select and apply appropriate tools for a particular application.

Unit Schedule

Week Activities Assessment
0   No formal assessment or activities are undertaken in week 0
1 Introduction  
2 Problem Solving  
3 Knowledge Representation  
4 Planning Assignment 1 due 28 March 2013
5 Soft Computing  
6 Evolutionary Algorithms  
7 Bayesian Networks  
8 Intelligent Decision Support Assignment 2 due 3 May 2013
9 Supervised Machine Learning  
10 Unsupervised Machine Learning  
11 Agent-Based Modeling  
12 Stochastic Problem Solving Assignment 3 due 31 May 2013
  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

Examination (3 hours): 70%; In-semester assessment: 30%

Assessment Task Value Due Date
Assignment 1 - Knowledge Representation and Planning 10% 28 March 2013
Assignment 2 - Bayesian Networks and Soft Computing 10% 3 May 2013
Assignment 3 - Machine Learning 10% 31 May 2013
Examination 1 70% To be advised

Teaching Approach

Lecture and tutorials or problem classes
This teaching and learning approach provides facilitated learning, practical exploration and peer learning.

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 - Knowledge Representation and Planning
    Description:
    A problem solving exercise on knowledge representation and planning.
    Weighting:
    10%
    Criteria for assessment:

    Correctness and completeness of answers to problems.

    Due date:
    28 March 2013
  • Assessment task 2
    Title:
    Assignment 2 - Bayesian Networks and Soft Computing
    Description:
    A problem solving exercise on Bayesian networks and soft computing.
    Weighting:
    10%
    Criteria for assessment:

    Correctness and completeness of submitted answers and/or Bayesian networks.

    Due date:
    3 May 2013
  • Assessment task 3
    Title:
    Assignment 3 - Machine Learning
    Description:
    A problem solving exercise on machine learning.
    Weighting:
    10%
    Criteria for assessment:

    Correctness and completeness of answers to machine learning problems.

    Due date:
    31 May 2013

Examinations

  • Examination 1
    Weighting:
    70%
    Length:
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:
    None

Learning resources

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:
  • Informal feedback on progress in labs/tutes
  • Graded assignments with comments
  • Solutions to tutes, labs and assignments

Extensions and penalties

Returning assignments

Resubmission of assignments

No resubmissions.

Referencing requirements

See Library Guides for Citing and Referencing athttp://guides.lib.monash.edu/content.php?pid=88267&sid=656564

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).

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.

Required Resources

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

Prescribed text(s)

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.

Recommended text(s)

Witten, I and Frank, E. (2005). Data Mining - Practical Machine Learning Tools and Techniques. (3rd Edition) Elsevier.

Korb, K and Nicholson, A. (2010). Bayesian Artificial Intelligence. (2nd Edition) CRC 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.

Your feedback to Us

Previous Student Evaluations of this Unit

Assessment weighting has been changed due to students' feedback.

If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp

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