Course Outline

CSC300 Practical Machine Learning

Course Coordinator:Paulo Petersen Saraiva (ppetersen@usc.edu.au) School:School of Science, Technology and Engineering

2024Semester 1

UniSC Moreton Bay

Blended learning Most of your course is on campus but you may be able to do some components of this course online.

Please go to usc.edu.au for up to date information on the
teaching sessions and campuses where this course is usually offered.

What is this course about?

Description

This course gives you a practical introduction to machine learning and deep learning. It introduces you to a variety of learning algorithms and how to use them. You will learn about the key stages of the machine learning process such as algorithm selection, feature selection, model building, diagnostics, cross-validation, and testing.

How will this course be delivered?

Activity Hours Beginning Week Frequency
Blended learning
Learning materials – Pre-recorded concept videos and associated activity 2hrs Week 1 13 times
Tutorial/Workshop 1 – On-Campus Computer workshop 2hrs Week 1 13 times

Course Topics

  • Machine learning pipelines
  • Supervised and unsupervised learning
  • Classification and recommendation systems
  • Neural networks and deep learning

What level is this course?

300 Level (Graduate)

Demonstrating coherence and breadth or depth of knowledge and skills. Independent application of knowledge and skills in unfamiliar contexts. Meeting professional requirements and AQF descriptors for the degree. May require pre-requisites where discipline specific introductory or developing knowledge or skills is necessary. Normally undertaken in the third or fourth full-time study year of an undergraduate program.

What is the unit value of this course?

12 units

How does this course contribute to my learning?

Course Learning Outcomes On successful completion of this course, you should be able to... Graduate Qualities Completing these tasks successfully will contribute to you becoming...
1 Explain common models and processing pipelines in machine learning applications Knowledgeable
2 Apply machine learning algorithms to design solutions for real problems Creative and critical thinker
Empowered
3 Compare benefits/drawbacks of different models and algorithms regarding real use cases Knowledgeable
Creative and critical thinker
Empowered
4 Analyse results and solutions to verify their correctness and impact on decision making Engaged
5 Report model selection, implementation, and evaluation in written communication. Creative and critical thinker
Empowered

Am I eligible to enrol in this course?

Refer to the UniSC Glossary of terms for definitions of “pre-requisites, co-requisites and anti-requisites”.

Pre-requisites

CSC201 and MTH212

Co-requisites

Not applicable

Anti-requisites

Not applicable

Specific assumed prior knowledge and skills (where applicable)

Not applicable

How am I going to be assessed?

Grading Scale

Standard Grading (GRD)

High Distinction (HD), Distinction (DN), Credit (CR), Pass (PS), Fail (FL).

Details of early feedback on progress

Students will complete individual weekly workshop activities under the guidance of the workshop facilitator, providing opportunities for rapid formative feedback throughout the semester.

Assessment tasks

Delivery mode Task No. Assessment Product Individual or Group Weighting % What is the duration / length? When should I submit? Where should I submit it?
All 1 Examination - not Centrally Scheduled Individual 10%
2 hours
Week 6 Online Assignment Submission with plagiarism check
All 2 Artefact - Technical and Scientific, and Written Piece Individual 40%
Code implementation and a brief report to explain the model and results
Week 11 Online Assignment Submission with plagiarism check
All 3 Artefact - Technical and Scientific, and Written Piece Individual 50%
Code implementation plus 1500 words
Exam Period Online Assignment Submission with plagiarism check
All - Assessment Task 1:Examination
Goal:
The exam will develop your ability to independently apply your skills and knowledge to solve familiar problem-based questions with confidence within a set time limit.
Product: Examination - not Centrally Scheduled
Format:
This examination consists of a set of questions on the use of machine learning models and algorithms. The questions are based on tutorial activities and course learning materials.
Criteria:
No. Learning Outcome assessed
1
Selection, adaption and design of solutions using principles of machine learning models and algorithms
2 3
2
Comparison, analysis and evaluation of given learning solutions and models
1 3 4
Generic Skills:
Problem solving, Applying technologies
All - Assessment Task 2:Data modeling
Goal:
Apply machine learning tools to learn models from data sets
Product: Artefact - Technical and Scientific, and Written Piece
Format:
You will be presented with a data-related challenge, and will use machine learning tools to learn and evaluate models from the data.
Criteria:
No. Learning Outcome assessed
1
Comparison of common machine learning models and algorithms
1
2
Selection and development of learning models for the given data
2
3
Analysis, interpretation and evaluation of the learned models
4
Generic Skills:
Problem solving, Applying technologies
All - Assessment Task 3:Machine learning project
Goal:
You will explore a case study and apply your knowledge of machine learning to design, justify and develop an application to meet the case study requirements.
Product: Artefact - Technical and Scientific, and Written Piece
Format:
1 software application (code) satisfying the requirements of the case study and 1 report with 1500 words on design decisions justifying the chosen learning models.
Criteria:
No. Learning Outcome assessed
1
Selection, adaption and design of solutions using principles of machine learning models and algorithms
3
2
Development of an executable learning program to satisfy the requirements in the case study
2
3
Analysis and evaluation of the proposed solution regarding the case study
4
4
Accurate communication and reporting of the proposed model.
5
Generic Skills:
Communication, Problem solving, Applying technologies

Directed study hours

A 12-unit course will have total of 150 learning hours which will include directed study hours (including online if required), self-directed learning and completion of assessable tasks. Student workload is calculated at 12.5 learning hours per one unit.

What resources do I need to undertake this course?

Please note: Course information, including specific information of recommended readings, learning activities, resources, weekly readings, etc. are available on the course Canvas site– Please log in as soon as possible.

Prescribed text(s) or course reader

Please note that you need to have regular access to the resource(s) listed below. Resources may be required or recommended.

Required? Author Year Title Edition Publisher
Required Aurélien Géron 2019 Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow 2 O'Reilly

Specific requirements

Not applicable

How are risks managed in this course?

Health and safety risks for this course have been assessed as low. It is your responsibility to review course material, search online, discuss with lecturers and peers and understand the health and safety risks associated with your specific course of study and to familiarise yourself with the University’s general health and safety principles by reviewing the online induction training for students, and following the instructions of the University staff.

What administrative information is relevant to this course?

Assessment: Academic Integrity

Academic integrity is the ethical standard of university participation.  It ensures that students graduate as a result of proving they are competent in their discipline.  This is integral in maintaining the value of academic qualifications. Each industry has expectations and standards of the skills and knowledge within that discipline and these are reflected in assessment.

Academic integrity means that you do not engage in any activity that is considered to be academic fraud; including plagiarism, collusion or outsourcing any part of any assessment item to any other person.  You are expected to be honest and ethical by completing all work yourself and indicating in your work which ideas and information were developed by you and which were taken from others. You cannot provide your assessment work to others. You are also expected to provide evidence of wide and critical reading, usually by using appropriate academic references.

In order to minimise incidents of academic fraud, this course may require that some of its assessment tasks, when submitted to Canvas, are electronically checked through Turnitin.  This software allows for text comparisons to be made between your submitted assessment item and all other work to which Turnitin has access.

Assessment: Additional Requirements

Eligibility for Supplementary Assessment

Your eligibility for supplementary assessment in a course is dependent of the following conditions applying:

The final mark is in the percentage range 47% to 49.4%
The course is graded using the Standard Grading scale
You have not failed an assessment task in the course due to academic misconduct

Assessment: Submission penalties

Late submission of assessment tasks may be penalised at the following maximum rate: 
- 5% (of the assessment task's identified value) per day for the first two days from the date identified as the due date for the assessment task. 
- 10% (of the assessment task's identified value) for the third day - 20% (of the assessment task's identified value) for the fourth day and subsequent days up to and including seven days from the date identified as the due date for the assessment task. 
- A result of zero is awarded for an assessment task submitted after seven days from the date identified as the due date for the assessment task. Weekdays and weekends are included in the calculation of days late. To request an extension you must contact your course coordinator to negotiate an outcome.

SafeUniSC

UniSC is committed to a culture of respect and providing a safe and supportive environment for all members of our community. For immediate assistance on campus contact SafeUniSC by phone: 07 5430 1168 or using the SafeZone app. For general enquires contact the SafeUniSC team by phone 07 5456 3864 or email safe@usc.edu.au.

The SafeUniSC Specialist Service is a Student Wellbeing service that provides free and confidential support to students who may have experienced or observed behaviour that could cause fear, offence or trauma. To contact the service call 07 5430 1226 or email studentwellbeing@usc.edu.au.

Study help

For help with course-specific advice, for example what information to include in your assessment, you should first contact your tutor, then your course coordinator, if needed.

If you require additional assistance, the Learning Advisers are trained professionals who are ready to help you develop a wide range of academic skills. Visit the Learning Advisers web page for more information, or contact Student Central for further assistance: +61 7 5430 2890 or studentcentral@usc.edu.au.

Wellbeing Services

Student Wellbeing provide free and confidential counselling on a wide range of personal, academic, social and psychological matters, to foster positive mental health and wellbeing for your academic success.

To book a confidential appointment go to Student Hub, email studentwellbeing@usc.edu.au or call 07 5430 1226.

AccessAbility Services

Ability Advisers ensure equal access to all aspects of university life. If your studies are affected by a disability, learning disorder mental health issue, injury or illness, or you are a primary carer for someone with a disability or who is considered frail and aged, AccessAbility Services can provide access to appropriate reasonable adjustments and practical advice about the support and facilities available to you throughout the University.

To book a confidential appointment go to Student Hub, email AccessAbility@usc.edu.au or call 07 5430 2890.

Links to relevant University policy and procedures

For more information on Academic Learning & Teaching categories including:

  • Assessment: Courses and Coursework Programs
  • Review of Assessment and Final Grades
  • Supplementary Assessment
  • Central Examinations
  • Deferred Examinations
  • Student Conduct
  • Students with a Disability

For more information, visit https://www.usc.edu.au/explore/policies-and-procedures#academic-learning-and-teaching

Student Charter

UniSC is committed to excellence in teaching, research and engagement in an environment that is inclusive, inspiring, safe and respectful. The Student Charter sets out what students can expect from the University, and what in turn is expected of students, to achieve these outcomes.

General Enquiries

  • In person:
    • UniSC Sunshine Coast - Student Central, Ground Floor, Building C, 90 Sippy Downs Drive, Sippy Downs
    • UniSC Moreton Bay - Service Centre, Ground Floor, Foundation Building, Gympie Road, Petrie
    • UniSC SouthBank - Student Central, Building A4 (SW1), 52 Merivale Street, South Brisbane
    • UniSC Gympie - Student Central, 71 Cartwright Road, Gympie
    • UniSC Fraser Coast - Student Central, Student Central, Building A, 161 Old Maryborough Rd, Hervey Bay
    • UniSC Caboolture - Student Central, Level 1 Building J, Cnr Manley and Tallon Street, Caboolture
  • Tel:+61 7 5430 2890
  • Email:studentcentral@usc.edu.au