DTSC71-306: Advanced Machine Learning

Description

This subject is designed to provide students with the knowledge and skills to develop applications of Deep Learning to Big Data in a modern business setting. Specifically, students will learn how Deep Learning models extract complex abstractions as data representations through a hierarchical learning process to learn and infer from Big Data datasets. Students will study how a key benefit of Deep Learning, the analysis and learning of massive amounts of unsupervised data, makes it a valuable tool for Big Data Analytics. The subject finishes with an investigation into the latest research being undertaken involving Deep Learning models.

Subject details

Type:	Postgraduate Subject
Code:	DTSC71-306
Faculty:	Bond Business School
Credit:	10
Study areas:	Actuarial Science and Data Analytics Business, Commerce, and Entrepreneurship

Learning outcomes

Recognise, document and model the inputs, outputs, relationships, boundaries, and data transformations of Big Data digital systems.
Design, implement, train and evaluate deep neural network models for business Big Data Analytics.
Apply the language, thinking and tools of Deep Learning to real-world problems.
Identify and rationalize the application of the appropriate specialized Deep Learning Architecture to Business Data systems.
Articulate ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a wide range of audiences within both academic and real-world settings

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): DTSC71-200: Data Science DTSC71-301: Applied Machine Learning Possess demonstrable knowledge in elementary probability theory, statistics, elementary calculus and linear algebra to the level of a unit such as STAT71-112 Quantitative Methods as well as basic data science concepts and techniques to the level of a unit such as DTSC71-200 Data Science
Restrictions:

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 STAT71-112 Quantitative Methods as well as basic data science concepts and techniques to the level of a unit such as DTSC71-200 Data Science

Restrictions: