Who should attend
This course 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.
About the course
Designed for professionals with some familiarity with machine learning (ML), this course will provide a deeper understanding of statistical learning theory and empirical risk minimisation, allowing participants to improve their ML models and algorithms. The course covers both theoretical considerations and hands-on, under-the-hood coding exercises.
About this course
This course introduces some advanced machine learning data models, algorithms and theoretical results. It focuses on the following key considerations:
Building data models with neural networks, deep neural network (DNN) architecture, generalised linear models and kernel methods Learning data models covering gradient-based algorithms and optimisation, backpropagation and constrained optimisation practice Improving model reliability using DNN structures to enable learning stability, and regularisation techniques Exploring why learned models can be trusted through the risk theory of learning-based models, looking at bias, variance, training and test evaluation.
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
This module covers the construction, computation and training of neural networks. You will develop hands-on experience of building neural network models and knowledge of the state-of-the-art NN models in different application areas.
Module 2 - Machine learning theory
This module covers a discussion of learning from experience and goes into depth on Hoeffding’s inequality to consider the bounds on reliability.
Module 3 - Convolutional neural networks
This module covers the motivation behind convolutional neural networks and focuses on the computation and backpropagation of a convolutional layer.
Module 4 - Transformer families
This module introduces how self-correlation can be useful in a learning model and how to represent the correlation and implement the model as a block of neural networks. Participants will get hands-on experience in building a family of neural network from scratch and applying a transformer model to a practical data set.
Module 5 - Generative models
This module introduces generative adversarial networks (GANs), featuring a definition of the GAN model, the core training steps of GANs and a detailed walkthrough of implementing a GAN and evaluating the results.
Key benefits of this microcredential
- Upgrade your machine learning models and projects – develop the knowledge and skills to build and understand more reliable models
- Gain an in-depth coverage of the theoretical models and considerations underpinning machine learning and some practical coding exercises to demonstrate them
- Complete as a self-contained course, or as a potential pathway to future postgraduate study.
This microcredential aligns with the two-credit point subject, Advanced Machine Learning (42894) in one of the following postgraduate offerings:
- Graduate Certificate of Professional Practice (C11298)
- Graduate Diploma of Professional Practice (C06136)
- Master of Professional Practice (C04404)
- Graduate Certificate of Technology (C11301)
- Graduate Diploma of Technology (C06137)
- Master of Technology (C04406)
This microcredential may qualify for recognition of prior learning at this and other institutions.
Videos and materials
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.