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.
Bond Business School
Actuarial Science and Data Analytics
Business, Commerce, and Entrepreneurship
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have
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.
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 DTSC12-200 Data Science
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