Availabilities:

2021 unit offering information will be available in September 2020

Unit description

Introduces students to machine learning. Students will take an algorithmic approach to machine learning in which real-world problems will be solved through machine learning techniques. Students will familiarise themselves with a wide range of algorithms and implement them for problem solving in Python/Octave.

Unit content

Topic 1: Intro to machine learning

Topic 2: Types of machine learning

Topic 3: Regression and prediction

Topic 4: Classification

Topic 5: Neural networks

Topic 6: Machine learning: best practices

Topic 7: Philosophy of machine learning

 

Learning outcomes

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

GA1: Intellectual rigour, GA2: Creativity, GA3: Ethical practice, GA4: Knowledge of a discipline, GA5: Lifelong learning, GA6: Communication and social skills, GA7: Cultural competence
On completion of this unit, students should be able to:GA1GA2GA3GA4GA5GA6GA7
1identify and evaluate the evolution and recent trends in computational intelligence and machine learning Knowledge of a disciplineLifelong learning
2critically analyse real-world machine learning problems and compare between a range of solutions in a variety of contexts CreativityKnowledge of a disciplineLifelong learning
3develop, create and implement machine learning solutions to complicated problems CreativityKnowledge of a discipline
4plan and implement parts of the tasks in a machine learning pipeline to complete a predictive analysis problem to satisfaction CreativityKnowledge of a discipline

On completion of this unit, students should be able to:

  1. identify and evaluate the evolution and recent trends in computational intelligence and machine learning
    • GA4: Knowledge of a discipline
    • GA5: Lifelong learning
  2. critically analyse real-world machine learning problems and compare between a range of solutions in a variety of contexts
    • GA2: Creativity
    • GA4: Knowledge of a discipline
    • GA5: Lifelong learning
  3. develop, create and implement machine learning solutions to complicated problems
    • GA2: Creativity
    • GA4: Knowledge of a discipline
  4. plan and implement parts of the tasks in a machine learning pipeline to complete a predictive analysis problem to satisfaction
    • GA2: Creativity
    • GA4: Knowledge of a discipline

Teaching and assessment

Notice

Intensive offerings may or may not be scheduled in every session. Please refer to the timetable for further details.

Southern Cross University employs different teaching methods within units to provide students with the flexibility to choose the mode of learning that best suits them. SCU academics strive to use the latest approaches and, as a result, the learning modes and materials may change. The most current information regarding a unit will be provided to enrolled students at the beginning of the study session.

Fee information

Domestic

Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.
Commencing 2021 Commonwealth Supported only. Student contribution band: 2

Fee paying courses
For POSTGRADUATE or UNDERGRADUATE full fee paying courses please check Domestic Postgraduate Fees OR Domestic Undergraduate Fees

International

Please check the international course and fee list to determine the relevant fees.

+