CAI402 Foundation of Machine Learning Programme – NZ2860 New Zealand Certificate in Study and Employment Pathways Version 2 (Level 4, 60 Credits) Course – CAI402 Foundation of Machine Learning (15 Credits) CAI402 A
CAI402 Foundation of Machine Learning Programme – NZ2860 New Zealand Certificate in Study and Employment Pathways Version 2 (Level 4, 60 Credits)
Course – CAI402 Foundation of Machine Learning (15 Credits)
CAI402 Assessment Group Presentation Weighting within the course: 100%
Objective This course aims to enable ākonga to build on knowledge and skills in machine learning, collaboration, and fundamental programming concepts.
This assessment evaluates your ability to research, select and summarise key concepts in machine learning and automation using credible sources, and to present your findings clearly as a team.
Learning Outcomes (LOs) Covered LO1 Research and summarise key concepts of machine learning and automation using information from credible sources.
Graduate Profile Outcomes (GPOs) Covered GPO1 Locate, select and analyse relevant information from a variety of sources, and apply this information by working independently and collaboratively, on context-relevant tasks and problems
GPO2 Construct a well-reasoned and researched argument relevant to their chosen field(s) and communicate it, using appropriate modes and media.
Assessment Matrix Learning Outcome Task Component Mapped GPOs Weighting (%) LO1: Research and summarise key concepts of machine learning and automation using information from credible sources 1. Research and Understanding of Key Concepts GPO1, GPO2 30% 2. Application to Real World Industries GPO1, GPO2 30% 3. Reflective Summary on Research GPO1 20% 4. Credible Sources, Referencing and Critical Evaluation GPO1 15% Presentation Structure and Visual Aids GPO2 5% Total 100% Grading The final grade will be determined by the score achieved in this assessment based on the following table. In order to meet the requirements of this course, ākonga | learners must achieve a minimum grade of 50% for all assessments. All assessments must be passed independently; marks are not aggregated or averaged across assessments. Ākonga | learners are permitted one attempt per assessment task.
If an ākonga | learner does not achieve a passing grade on the first attempt, they may be provided with one opportunity to re-sit. To be eligible to re-sit a grade between 40 – 49% on the first attempt is required. 50% is the maximum grade awarded for a re-sit.
Please note:
Failure to achieve a passing result after the re-sit may result in non-completion of the course and you may need to re-enrol in the course to progress in the programme of study.
Grade Range Pass/Fail A Meet all course requirements, range (80+) Pass B Meet all course requirements, range (65-79%) Pass C Meet all course requirements, range (50-64%) Pass D Did not meet all course requirements, range (40-49%) Fail E Did not meet all course requirements, mark range (0-39%) Fail Assessment Instructions This assessment is an open-book activity, you can use your own course and review notes as well as offline or online resources, such as textbooks or online journals. You can always ask your tutor if you need further explanation about forming a group or if the instructions are unclear. Your work should not be plagiarised. Plagiarism includes copying material without acknowledging it, copying from another student, getting another person to help you with your assessment, using material from commercial essays or assignment services, or using AI to create the answers. The purpose of this assessment is to assess your knowledge. In the event Yoobee suspects collusion, this will be addressed. For more information on plagiarism, please refer to the Student Handbook. Marks and feedback will be returned within 15 days of the submission date. By completing and submitting an assessment you are authenticating that the work is original and does not violate plagiarism or copyright law. Authenticity is checked where any breaches of academic integrity are suspected. Please refer to the Student Handbook for further information Submission Instructions Please submit the following to your LMS (Learning Management System) by the due date:
Group Presentation o Video presentation (.mp4 format), 10-12 minutes in length for groups of 2 or 15-18 minutes in length for groups of 3. o Copy of the presentation slides in PDF or .pptx format Assessment Tasks Completion Timeline Week What needs to be done What needs to be submitted Week 1 Assessment released on LMS. Form groups of 2-3, assign roles and responsibilities, and begin research.
—- Week 2 Continue research, draft slides ——– Week 3 Complete reflective summary and record audio narration. Format referencing in APA style, and ensure all tasks are addressed.
———– Week 4 Finalise and submit assessment. Group presentation (.mp4 format) plus copy of presentation slides (.PDF or .pptx format) submitted on the LMS Score Better In CAI402 Machine Learning Assessment Short Description: Hire NZ Native Experts 24/7.
HIRE EXPERT Task Description: Form a group of 2 or 3 to prepare and deliver a presentation exploring machine learning and automation in real life. Each group member must participate equally in the research, and present for at least 5 minutes.
Presentation Structure And Requirements Your presentation should be delivered showing good presentation principles, including:
A Title Slide and Introduction: Your presentation must start with an introduction which includes: o A presentation title, the names and student IDs of all group members, the course name, and submission date. o A brief outline of what the presentation will cover. Clear speaking, with confident and engaging delivery Equal participation for all team members (at least 5 minutes of presentation time each) Presentation Structure and Visual Aids: o Logical flow of content o Clear and uncluttered slides o Use of appropriate and relevant visuals (e.g. charts, diagrams etc.) which add value to the subject you are discussing. Presentation Tasks
- Research and Understanding of Key Concepts:
i. Explain in your own words what machine learning is. ii. Compare supervised and unsupervised learning and provide examples of each. iii. Explain what automation means in this context and how Python can be used to automate tasks relevant to intelligent systems.
- Application to Real World Industries:
Choose at least two distinct real-world sectors such as healthcare, transport, agriculture, retail, finance, education etc. For each sector, include:
A description of a specific problem or issue addressed by machine learning and/or AI automation. The type and likely source of data, whether it would be labelled or unlabelled. An explanation of the machine learning task and why it is appropriate for this industry and problem or issue. Explain the likely benefits, limitations and risks in using machine learning and automation in these contexts. Give at least one benefit and one limitation or risk (for example bias, data quality, maintenance or privacy) Supporting evidence with at least one cited source for the information and for any datasets mentioned. 3. Reflective summary on research:
Explain how your group searched for, evaluated and selected information to use for the presentation. Describe at least one challenge your group faced in collaborating or communicating to research and create the presentation. Explain how you overcame the challenge(s) described. 4. Use of Credible Sources and Referencing:
Use at least three credible sources to research your topic. Evaluate credibility using CARS or CRAAP, including any datasets you use. Summarise key credibility reasons in your notes or slides. Use APA 7 in-slide citations for any facts, figures or images that are not common knowledge. Include a final APA 7 reference list as a last slide in the presentation. CAI402 Assessment Marking Rubrics Task Weighti ng A (80-100%)
B (65-79%)
C (50-64%)
D (40-49%)
E (0-39%)
- Research and Understan ding of Key Concepts (LO1)
30% Accurate, clear explanation s in plain language. Correct explanation
of supervised and unsupervise
d with
relevant examples. Automation defined appropriatel
y and
Python’s role explained clearly.
Mostly accurate with minor gaps. Examples appropriate but under‑develo ped. Small omissions or imprecision in automation or Python explanation. Basic and partly accurate coverage. Examples are generic or unclear. Difference s between learning types not well explained.
Major inaccuraci
es or omissions across concepts. Examples misleading or poorly chosen.
Very limited understandi ng. Key
definitions
missing or incorrect.
- Applicatio n to Realworld Industries
(LO1)
30% Two or more sectors discussed. For each, a specific, well-scoped
problem; data type and likely source
clearly stated with labelled or unlabelled
status; ML task correctly
identified and convincingl
y justified for the
context; benefits and limitations/ri
sks are
Two sectors discussed. All elements present with minor gaps in specificity or depth. ML task is appropriate but
justification is brief. Benefits
and limitations or risks mostly relevant. Evidence provided for each sector, though one citation or dataset attribution may be light.
Two sectors mentioned
but treatment is uneven: one or
more
elements
thin or generic in each sector. ML task identified with limited or
unclear
justificatio
n. Benefits and
limitations or risks are surfacelevel.
Evidence present
Only one sector, or multiple required elements missing in one or
both sectors. ML task is mismatche
d to the problem or largely unjustified. Benefits and
limitations
or risks are vague or off-point. Little or no sectorspecific evidence; datasets not cited.
Application s are absent or inaccurate. Problems,
data and task are incorrect or missing.
Claims are unsupporte
d; no
credible sources or dataset citations.
concrete and balanced;
all significant claims supported with at least one source per sector and any
datasets explicitly cited.
but minimal or not clearly tied to claims. 3. Reflective summary on research (LO1) 20% Clear account of search, selection and synthesis decisions. Shows how
collaboratio n improved clarity and accuracy. Specific challenge explained with a clear link to better evidence or explanation.
Covers search and selection with minor gaps.
Collaboration described generally. Challenge linked partially to improvement.
Basic description of process. Limited detail on collaborati on or learning. Weak link
to
improvem ent.
Minimal reflection with little evidence of process or
improvem ent.
Unclear
link to
LO1.
No meaningful reflection or link to LO1. 4. Credible Sources,
Referencing and
Critical
Evaluation
(LO1)
15% Three or more credible sources. Correct in‑slide citations and
complete
APA 7
reference
list. Explicit, thoughtful
application of CARS or CRAAP to
justify credibility, including datasets.
At least three credible sources. APA mostly correct with minor issues. Credibility considered but not consistently.
Three sources with several APA
errors. Credibility discussion
brief or generic.
Fewer than three credible sources or significant
APA errors.
Weak or
missing
credibility evaluation.
Citations or reference list missing, or sources not credible.
Presentation Structure,
Visual Aids and Time
Management
5% Clear, logical progression signposted from introduction
to conclusion. Slides concise and uncluttered with consistent formatting. Visuals are relevant, correctly labelled and interpreted
in speech,
clearly
strengtheni
ng key points. Finishes within the allotted time without rushing.
Generally logical structure with minor ordering or signposting issues. A few slides slightly text-heavy. Visuals mostly relevant and labelled, with minor gaps in explanation. Completes within the allotted time with minor pacing issu
