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DTSC13-306: Modern Machine Learning Models

Description

This subject is designed for students who already have a basic understanding of machine learning and want to deepen their knowledge using more advanced techniques. The subject focuses on advanced machine learning methods that are relevant and effective in many real-life and business applications. Students will be provided the necessary tools to wrangle data, implement and train machine learning models, and evaluate the performance and feasibility of these models in the context of the environment where these models are going to be applied. Advanced visualisation tools will be used to create dynamic visual representations of data.

Subject details

Type: Undergraduate Subject
Code: DTSC13-306
EFTSL: 0.125
Faculty: Bond Business School
Semesters offered:
  • September 2024 [Standard Offering]
Credit: 10
Study areas:
  • Actuarial Science and Data Analytics
Subject fees:
  • Commencing in 2024: $4,260.00
  • Commencing in 2025: $4,460.00
  • Commencing in 2024: $5,730.00
  • Commencing in 2025: $5,990.00

Learning outcomes

  1. Create common models to extract patterns from data using sophisticated machine learning techniques and evaluate their effectiveness.
  2. Analyse the feasibility and usefulness of predictive models in the context for which they were created.
  3. Design solutions for common business problems using combinations of machine learning algorithms.
  4. Use computational infrastructure to implement solutions that involve big data for common business problems.
  5. Create static and dynamic common visualisations to convey clear information to management and other users.

Enrolment requirements

Requisites:

Nil

Assumed knowledge:

Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.

Assumed Prior Learning (or equivalent):

Basic Python (equivalent to DTSC11-100) Basic concepts of data science (equivalent to DTSC12-200).

Restrictions:

Subject dates

  • Standard Offering
    Enrolment opens: 14/07/2024
    Semester start: 09/09/2024
    Subject start: 09/09/2024
    Cancellation 1: 23/09/2024
    Cancellation 2: 30/09/2024
    Last enrolment: 22/09/2024
    Withdraw - Financial: 05/10/2024
    Withdraw - Academic: 26/10/2024
    Teaching census: 04/10/2024
Standard Offering
Enrolment opens: 14/07/2024
Semester start: 09/09/2024
Subject start: 09/09/2024
Cancellation 1: 23/09/2024
Cancellation 2: 30/09/2024
Last enrolment: 22/09/2024
Withdraw - Financial: 05/10/2024
Withdraw - Academic: 26/10/2024
Teaching census: 04/10/2024