CSC300 Practical Machine Learning

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?

Course Topics

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

Microcredential Information

Not applicable

How am I going to be assessed?

Grading Scale

Standard Grading (GRD)

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 trimester.

Assessment tasks

2 hours

1500 words including code and brief report

Code implementation plus 1500 words

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

You need regular access to the resource(s) below. Many texts are available as ebooks through the Library at no additional cost.

Specific requirements

Not applicable

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:
(a) The final mark is in the percentage range 47% to 49.4%; and
(b) The course is graded using the Standard Grading scale

Assessment: Submission penalties

Late submissions may be penalised up to and including the following maximum percentage of the assessment task’s identified value, with weekdays and weekends included in the calculation of days late:
(a) One day: deduct 5%;
(b) Two days: deduct 10%;
(c) Three days: deduct 20%;
(d) Four days: deduct 40%;
(e) Five days: deduct 60%;
(f) Six days: deduct 80%;
(g) Seven days: A result of zero is awarded for the assessment task.

The following penalties will apply for a late submission for an online examination:
Less than 15 minutes: No penalty
From 15 minutes to 30 minutes: 20% penalty
More than 30 minutes: 100% penalty

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

For course-specific questions, contact your teaching staff or Course Coordinator.  

For other enquiries or to access support, please contact Student Central: 

• UniSC Student Central
• UniSC Adelaide Student Central