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DTSC13-301: Machine Learning in Business

Description

Unprecedented volumes of data are being created on an almost daily basis and the amount of data we generate is expected to double every two years. This ‘Big Data’ has the power to change the way we work, live, and think. This subject is designed to provide students with the knowledge and skills to analyse Big Data in a variety of business contexts. Specifically, mathematical and practical applications of Artificial Neural Networks, Support Vector Machines, Natural Language Processing and Ensemble Decision Tree techniques are explored.  Valuable skills in the use of these techniques are reinforced with practical application.

Subject details

TypeUndergraduate
CodeDTSC13-301
EFTSL0.125
FacultyBond Business School
Semesters offered
  • May 2022 [Standard Offering]
Credit10
Study areas
  • Business and Commerce
Subject fees
  • Commencing in 2020: $5,010

Learning outcomes

1. Recognise and communicate the inputs, outputs, relationships, boundaries, and data transformations of digital systems.
2. Design, train and use neural networks, SVN and ensemble tree models for business data systems.
3. Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
4. Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
5. Apply the communication framework for translating data analysis into decision making outcomes.
6. Articulate ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.

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):

Possess demonstrable knowledge in elementary probability theory, statistics, elementary calculus and linear algebra to the level of a unit such as STAT11-112 Quantitative Methods as well as basic data science concepts and techniques to the level of a unit such as INFT12-216 Data Science

Restrictions: ?

Nil

Subject outlines

Subject dates

Standard Offering
Enrolment opens15/03/2020
Semester start25/05/2020
Subject start25/05/2020
Cancellation 1?08/06/2020
Cancellation 2?15/06/2020
Last enrolment07/06/2020
Withdraw – Financial?20/06/2020
Withdraw – Academic?11/07/2020
Teaching census?19/06/2020