Artificial Intelligence and Machine Learning

Page 1 Kaplan Business School Assessment Outline
Assessment 2 Information

Subject Code: DATA4800
Subject Name: Artificial Intelligence and Machine Learning
Assessment Title: Individual Evaluation Activity (Evaluating Neural Network Models)
Assessment Type: Individual Report
Choose an item.: 1000 Words (+/-10%)
Weighting: 30 %
Total Marks: 30
Submission: Turnitin
Due Date: Individual report via Turnitin due Tuesday, Week 11 23.55pm AEST

Your Task
Evaluate the Neural Network based predictive modelling capability in the Orange Data Mining
Application.
The report is worth 30 marks (see rubric for allocation of these marks).
Assessment Description
There has been a recent advent of Neural Networks including applications in deep learning.
Analytics professionals can run basic Deep Learning applications via the browser and no-code
platforms as the algorithms use hardware accessed via the cloud to provide the required
performance.
Assessment Instructions
You have been introduced to the Orange Data Mining application in class. Please ensure you have
downloaded it and are familiar with its operation. You will also be provided with a dataset containing
images.
Image Classification
a. Construct a predictive model using the Image Analytics widgets in Orange.
b. Classify the images using your model
.
Re- analyse dataset used in Assessment 1
a. Construct a predictive model using the Neural Network widget in Orange.
b. Classify the outcomes using your model
.
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Evaluate the effectiveness in both cases in terms of
a. Accuracy
b. Utility
c. Method used
d. ease of use, and
e. cognitive load.
Recommend improvements or suggest other applications.
Summary
Page 3 Kaplan Business School Assessment Outline
Important Study Information
Academic Integrity Policy
KBS values academic integrity. All students must understand the meaning and consequences
of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct
Policy.
What is academic integrity and misconduct?
What are the penalties for academic misconduct?
What are the late penalties?
How can I appeal my grade?
Click here for answers to these questions:
http://www.kbs.edu.au/current-students/student-policies/.
Word Limits for Written Assessments
Submissions that exceed the word limit by more than 10% will cease to be marked from the point
at which that limit is exceeded.
Study Assistance
Students may seek study assistance from their local Academic Learning Advisor or refer to the
resources on the MyKBS Academic Success Centre page. Click
here for this information.
Page 4 Kaplan Business School Assessment Outline
Assessment Marking Guide

DATA 4800
Assessment 2
Rubric
/30
Evaluation of app and forecast results
0-10 10-20 /20
Has demonstrated limited achievement:
Did not load data into Orange
Was unable to identify information relevant
to facilitate the understanding of Neural
Networks.
Did not develop model and classify images
Did not compare results between
assessment 1 and Neural Network Model
Has achieved all or most of:
Loaded data into Orange and identified
relevant widgets.
Identified information which is relevant to
building the required models
Developed model and classified images.
Compared results with algorithms used in
Assessment 1.
Recommendations, Summary and Structure
0-5 5-10 /10
Has demonstrated limited achievement:
Unable to make recommendations for
improvements of prediction.
Not a concise report with relevant headings.
Has achieved all or most of:
Useful recommendations based on
results obtained.
Concise and relevant information in the
report.
Well-structured and readable report with
relevant headings.