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This microcredential introduces some advanced machine learning data models, algorithms and theoretical results. It focuses on key considerations, such as building data models with neural networks, deep neural network (DNN) architecture and generalised linear models and kernel methods and learning data models covering gradient-based algorithms and optimisation, backpropagation and constrained optimisation practice.
It also considers improving model reliability using DNN structures to enable learning stability and regularisation techniques, as well as exploring why learned models can be trusted through the risk theory of learning-based models, looking at bias, variance, training and test evaluation.
This microcredential aligns with the 2 credit point subject, Advanced Machine Learning (42894) in the Graduate Certificate in Professional Practice (C11298), Graduate Diploma of Professional Practice (C06136), Master of Professional Practice (C04404), Graduate Certificate in Technology (C11301), Graduate Diploma in Technology (C06137) and Master of Technology (C04406).
This microcredential may qualify for recognition of prior learning at this and other institutions.
This microcredential is suitable for professionals from a wide range of sectors and backgrounds, who have completed the Machine Learning Foundations microcredential, or otherwise have some professional experience in the field and are comfortable working in Python.
UTS microcredentials are developed for professionals with a capacity to undertake postgraduate tertiary education.
Full price: $1,595.00 (GST-free)*
*Price subject to change. Please check price at time of purchase.
During this course you will meet and work with a dedicated course facilitator who will support your learning and engagement with teaching resources designed by the lead academic and team of experts from the Faculty of Engineering and IT.
The course is structured into five modules. Each module comprises self-study materials and facilitated online sessions. The five modules and key topics covered are:
Module 1 - Neural networks
Module 2 - Machine learning theory
Module 3 - Convolutional neural networks
Module 4 - Transformer families
Module 5 - Generative models
This course includes weekly, live, one-hour online tutorials and one-hour weekly Q&A sessions facilitated by an expert UTS academic, supporting self-study and online learning activities.
Both theoretical and practical learning are covered. The course provides a systematic view of the whole life-cycle of a data model, from the design motivation to the dynamics in the learning process and the evaluation and how reliable the evaluation results are. On the practical side, participants will translate mathematical notions into data structures and programs in a digital computer, which allows them to ‘open the hood’ of the data models and get hands-on experience to examine piece-by-piece how the models perform learning.
Regular formative quizzes throughout the microcredential will allow learners to gauge their progress.
Upon successful completion of this course you will be able to design machine learning algorithms with practical implementation for professional contexts.
The graded assessment task comprises an individually written assessment on implementing one of the advanced machine learning algorithms, i.e., building a data model and adapting the model to observed data - according to the machine learning framework. The model will be tested against data and assessed using the evaluation criteria introduced in the course. The implementation process and evaluation are to be summarised in a report.
Length: 2,000 words
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