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Dr Grace Rumantir

Campus Lecturer

Caulfield

Grace Rumantir

Contact hours: Thursday 2-4pm (in H7.08)

Tutors

Caulfield

Minh Le

Contact hours: Friday 2-3pm (in H7.87)

Academic Overview

Learning Objectives

At the completion of this unit students will:

  • be able to differentiate between supervised and unsupervised learning;
  • know how to apply the main techniques for supervised and unsupervised learning;
  • know how to use statistical methods for evaluating data mining models;
  • be able to perform data pre-processing for data with outliers, incomplete and noisy data;
  • be able to extract and analyse patterns from data using a data mining tool;
  • have an understanding of the difference between discovery of hidden patterns and simple query extractions in a dataset;
  • have an understanding of the different methods available to facilitate discovery of hidden patterns in a dataset;
  • have developed the ability to preprocess data in preparation for data mining experiments;
  • have developed the ability to evaluate the quality of data mining models;
  • be able to appreciate the need to have representative sample input data to enable learning of patterns embedded in population data;
  • be able to appreciate the need to provide quality input data to produce useful data mining models;
  • have acquired the skill to use the common features in data mining tools;
  • have acquired the skill to use the visualisation features in a data mining tools to facilitate knowledge discovery from a data set;
  • have acquired the skill to compare data mining models based on the results on a set of performance criteria;
  • be able to work in a team to extract knowledge from a common data set using different data mining methods and techniques.

Graduate Attributes

Monash prepares its graduates to be:
  1. responsible and effective global citizens who:
    1. engage in an internationalised world
    2. exhibit cross-cultural competence
    3. demonstrate ethical values
  2. critical and creative scholars who:
    1. produce innovative solutions to problems
    2. apply research skills to a range of challenges
    3. communicate perceptively and effectively

Assessment Summary

Examination (3 hours): 60%; In-semester assessment: 40%

Assessment Task Value Due Date
Unit Test 20% 15 September 2011
Group Assignment 20% 13 October 2011
Examination 1 60% To be advised

Teaching Approach

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

Feedback

Our 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
  • Test results and feedback
  • Quiz results
  • Solutions to tutes, labs and assignments

Your feedback to Us

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:
http://www.monash.edu.au/about/monash-directions/directions.html
http://www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html

Previous Student Evaluations of this unit

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

Unit Schedule

Week Activities Assessment
0   No formal assessment or activities are undertaken in week 0
1 Unit Adminstration and Introduction to Data Mining  
2 Model Building  
3 Model Evaluation  
4 Data Preprocessing  
5 Data Preprocessing  
6 Classification  
7 Clustering  
8 Unit Test (in lecture time slot) Assessment Task 1: Unit Test
9 Association Rules Mining (1) Assignment 2: Stage 1 Interview (in tutorial time slot)
10 Association Rules Mining (2)  
11 Web Mining Assignment 2: Stage 2 Submission
12 Data Mining and Information Visualization  
  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.

Assessment Requirements

Assessment Policy

To pass a unit which includes an examination as part of the assessment a student must obtain:

  • 40% or more in the unit's examination, and
  • 40% or more in the unit's total non-examination assessment, and
  • an overall unit mark of 50% or more.

If a student does not achieve 40% or more in the unit examination or the unit non-examination total assessment, and the total mark for the unit is greater than 50% then a mark of no greater than 49-N will be recorded for the unit

Assessment Tasks

Participation

  • Assessment task 1
    Title:
    Unit Test
    Description:
    Closed-book unit test to be conducted in the lecture time slot in Week 8.
    Weighting:
    20%
    Criteria for assessment:
    Due date:
    15 September 2011
  • Assessment task 2
    Title:
    Group Assignment
    Description:
    This assignment requires students to use the data mining tool, WEKA, to explore several models and then choose one that will likely to produce the best models for a given data set.
    Weighting:
    20%
    Criteria for assessment:

    The assignment will be in paired groups. 

    Stage 1: Group formation and understanding the assessment tasks - non assessable.  All group members will receive the same marks. If there are issues/concerns about individual contributions within a group, a peer evaluation form will be used.

    Stage 2: Submission - 20%.

    Students will be assessed on:

    - The degree to which the submission meet the assignment specification
    - The quality of the data preprocessing and the design of experiments
    - How well the experiments are conducted and summarised
    - How well the results of the experiments are analysed and documented

    Further assessment criteria and marking sheet will be made available on the unit Moodle site.

    Due date:
    13 October 2011

Examinations

  • Examination 1
    Weighting:
    60%
    Length:
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:
    None
    Remarks:
    to be conducted in the formal examination period

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).

Extensions and penalties

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.

Returning assignments

Students can expect assignments to be returned within two weeks of the submission date or after receipt, whichever is later

Other Information

Policies

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:
http://policy.monash.edu.au/policy-bank/academic/education/index.html

Key educational policies include:

Student services

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 www.monash.edu.au/students The Monash University Library provides a range of services and resources that enable you to save time and be more effective in your learning and research. Go to http://www.lib.monash.edu.au or the library tab in my.monash portal for more information. 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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