ASSESSMENT GUIDE
Unit Code: ITEC203 Introduction to Data Science and Machine Learning
Study Period: S1 2022
Assessment number (3)
Assessment Artefact: Code and Presentation
Weighting [40%]
Why this assessment?
What are the types of employability skills that I will acquire upon completion of this
assessment?
Assessment Overview:
Purpose, as written in the EUO
Due date: 17/06/2022, 5pm, on Friday of Week 15
Weighting: 40%
• Opportunity to apply theory into practice
• Exposure to real-life scenario
• Enhance the understanding of theoretical concepts introduced in workshops – the definition of each
problem, the logic of the algorithms, the meaning of their parameters and hyperparameters, the cons and
pros of them.
• Build up students’ coding and problem-solving skills with coding practice.
• Build up students’ documentation skills with report output.
• The feedback from this assessment will help students to be ready to co
ect any conceptual
misunderstanding and apply in more scenarios when taking roles e.g. data engineer in the job markets.
Skill Type
Developed critical and analytical thinking ☑
Developed ability to solve complex problems ☑
Developed ability to work effectively with others ☑
Developed confidence to learn independently ☑
Developed written communication skills ☑
Developed spoken communication skills ☑
Developed knowledge in the field study ☑
Developed work-related knowledge and skills ☑
2
Length and/or format: Document in the format of Jupyter notebook including Python source codes
and recorded presentation – about 5 minutes
Learning outcomes assessed LO3, LO4
Graduate attributes assessed GA2, GA5, GA8
How to submit: Via LEO
Return of assignment: Via LEO as final marks
Assessment criteria: Ru
ic: see end of document
Context
Suppose the students take a Data Engineer role in a company. Doing supervised learning, e.g.,
classification, would be a daily task for some of the real-world projects. So this assignment would enhance
the students’ understanding on the theoretical knowledge introduced in the workshops. In addition, from
those hands-on practice, students would gain deeper insights on how to apply machine learning algorithms
to solve real-world problems.
Instructions
Provide instructions for students for completing assignment
This assignment will focus on applying two Multiclass classification algorithms on the MNIST datasets. Tasks of this
assignment include
1. Split the whole dataset into training/testing dataset either by yourself or by functions provided by python
scikit-learn packages, explain in details what your code is doing. (5%)
2. Exploit the performance of kNN classifier on the dataset (15%)
a. Set k=10 which is the ground-truth number of categories in the dataset, then apply kNN classifier
(using functions from Python package), explain in detail the meaning of parameters for the function
interface you choose. (5%)
. Evaluate the performance with the metrics introduced in workshop (also using the metric functions
provided by Python scikit-learn package), explain in detail why a specific evaluation metric is picked
and what you can tell from the results about the model. (5%)
c. Experimenting with different values of k, and comparing the performance. What can you discover
from the comparison? Is there any inspiration on choosing k? (5%)
3. Exploit the performance of SVM classifier on the dataset (10%)
a. Try appropriate SVM classifiers from https:
scikit-learn.org/stable/modules/svm.html, and explain
in details the meaning of parameters of the function interface you choose. (5%)
. Compare the performance of the SVM classifier and the kNN classifier. What have you discovered
from such comparison, such as any clues for your future decision on what model to use in what
situation? (5%)
4. record presentation: using succinct language to explain what you’ve done and what you’ve discovered in
5 minutes. (10%)
Structure
Prepare a Jupyter notebook and video recordings for this assignment. The structure of the Jupyter notebook should
alternate texts and python codes and cover topics listed the in specific tasks above. The video recording is used to
confirm academic integrity. Each cell in the Jupyter notebook needs to be explained in the video.
How do I submit?
Submit to Assessment 3 via LEO assessment tile
Note that: To make sure the submission satisfies academic integrity, the code will be compared to other students’
https:
scikit-learn.org/stable/modules/svm.html
3
submission in Turnitin.
Submission checklist
I have formatted my report as per the specifications ☐
I have checked my Turnitin report and taken appropriate actions to ensure that the submission
satisfies academic integrity
☐
I have actioned feedback advice provided to me from labs feedback and assignment 2 (if
applicable)
☐
I have submitted my work before the due date/time ☐
I have submitted feed forward template along with my assignment submission ☐
Feed Forward Template (example)
A template for students to use and act on feedback and provide recommendations for improvement.
Note
This is a task for any instance of follow-on assignment (assessment 2 and 3). This must be submitted as the first
page of the follow-on assignment (assessment 2 and 3) to ensure you acted on the feedback provided to you in the
previous assignment (this is not counted as part of the assessment word count).
How did you act on the feedback?
Feedback is an important component of learning. Please consider the feedback you received in your last
assignment and provide a response on how you acted on, or intend to act upon, that feedback, and how it has
informed the cu
ent assignment task. Submit this sheet along with your assignment.
Questions Your learning from the previous assignment feedback
How have you acted on the feedback from
previous assignment to improve your work
in this assignment?
(e.g. based on my previous feedback, I made sure that I
supported my discussion, position, ideas, concepts with peer
eviewed journal references in this assignment)
What is your expectation around the type of
feedback that enhances your learning?
(e.g. I want to know where I made a mistake and how I can
co
ect them and not make the same mistake again i.e. I want
specific feedback that will help me to improve my learning and
performance in the next assignment)
Did you have any difficulty understanding
or acting on previous feedback? Please be
as specific as possible so that you can gain
further feedback/clarify anything you do not
understand in the feedback
(e.g. feedback provided in my previous assignment was very
generic I did not know how to improve my work. So, I would
like the teacher to explain more on xxxx aspects of the
feedback or I would like an opportunity to have a dialogue to
understand the feedback)
4
Some Helpful Websites and Resources
Add in a couple of places to go for more info
Model Evaluation: https:
scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
Cross-validation: https:
scikit-learn.org/stable/modules/cross_validation.html
Grid search: https:
scikit-learn.org/stable/modules/grid_search.html
Who can help me?
Studiosity
Academic skills Unit (ASU)
Places –
NLiC Maoying Qiao ( XXXXXXXXXX)
LiC Zijing Chen ( XXXXXXXXXX)
Online Facilitator Maoying Qiao
Lab demonstrator Zijing Chen
I’m having problems
Special Consideration: This form is used by students to apply for Special Consideration for
assessable work in studies at Australian Catholic University. Approval of such applications will
only be granted to students who are legitimately disadvantaged in their assessment due to
exceptional and unforeseen circumstances beyond their control.
Referencing
All referencing should be in ACU Harvard style; however if you are coming from another faculty, you may choose
to use your usual referencing style. If this is the case you must indicate at the top of your reference list what
eferencing style you are using (e.g. APA, MLA, Chicago, etc).
Please ensure your assignment makes use of in-text citations and a reference list. Missing citations or references
is equivalent to plagiarism.
Criteria
The full criteria is compiled in a ru
ic, which can be found on the following page/s.
https:
scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
mailto: XXXXXXXXXX
https:
units.acu.edu.au/__data/assets/word_doc/0006/620655/SC_Application_for_Special_Consideration_ XXXXXXXXXXdocx
https:
libguides.acu.edu.au
eferencing/harvard
5
Ru
ic for [insert assessment item title and weighting]
Relevant LO/GAs Criterion (related to
a single GA from
the related LO –
one GA per criterion
Does not meet
expectations
Meets
expectations
Exceeds expectations
NN PA CR DI HD
GA5
LO3
Weight=15 marks
TL=3
Learning stage = I
and D
Demonstrate co
ect
understanding of the
concepts of
classification, kNN,
SVM, training-testing
splitting, evaluation
Fail to adequately
demonstrate co
ect
understanding of the
concepts of
classification, kNN,
SVM, training-testing
splitting, evaluation
(0 – 7.35)
Adequately
demonstrate co
ect
understanding of the
concepts of
classification, kNN,
SVM, training-testing
splitting, evaluation
(7.5 – 9.6)
Credibly demonstrate
co
ect understanding
of the concepts of
classification, kNN,
SVM, training-testing
splitting, evaluation
(9.75 – 11.1)
Distinctively
demonstrate co
ect
understanding of the
concepts of
classification, kNN,
SVM, training-testing
splitting, evaluation
(11.25 – 12.6)
Highly distinctively
demonstrate co
ect
understanding of the
concepts of
classification, kNN,
SVM, training-testing
splitting, evaluation
(12.75 – 15)
GA8
LO3
Weight=15 marks
TL=3
Learning stage = I
and D
Demonstrate python
programming skills by
experimenting with
elated functions in
scikit-learn package for
classification and
evaluation as well as
model selection.
Fail to adequately
demonstrate python
programming skills by
experimenting with
elated functions in
scikit-learn package for
classification and
evaluation as well as
model selection.
(0 – 7.35)
Adequately
demonstrate python
programming skills by
experimenting with
elated functions in
scikit-learn package for
classification and
evaluation as well as
model selection.
(7.5 – 9.6)
Credibly demonstrate
python programming
skills by experimenting
with related functions
in scikit-learn package
for classification and
evaluation as well as
model selection.
(9.75 – 11.1)
Distinctively
demonstrate python
programming skills by
experimenting with
elated functions in
scikit-learn package
for classification and
evaluation as well as
model selection.
(11.25 – 12.6)
Highly distinctively
demonstrate