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The Institute of Machine Learning (IML) has been established and funded in cooperation with and served as a bridge between academia and industry to help accelerate digital transformation by adopting innovative technologies which are inspired by Coherence Learning that gives corporates, businesses, and enterprises to observe trends, business operation patterns, and customer behaviour and fosters the development of new products and services through continuous delivery of R&D capabilities to bring productivity and creativity to a wide variety of applications with the power of AI.

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Coherence Learning is a novel deep learning model originated from Wavelet Deep Neural Network (WDNN) by Sparse Gaussian Wavelet Activations. It provides AI automated intelligence and high-performance deep learning with a new viewpoint on the success of the state-of-the-art techniques of initialization, time-frequency data analytics, sophisticated non-linear parameterised functions modelling, fast self-learning, and optimisation to address the issues of current methods with slow learning, inaccuracy, convergence to a local minimum, over-parameterization, and high computational complexity.

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The creation of Coherence Learning with a coherent scientific theory opens a new direction to construct multi-dimensionality and multi-functionality AI model by analysing all input and output datasets and model parameters in time-frequency domain with coherent accumulation to effectively and adaptively reduce noise and bias that could raise the power and capabilities of machine learning. This innovative technology can be applied to improve automation systems, communication systems, quantum machine learning, medical imaging applications and natural language processing.

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Coherence Learning allows training, learning, testing to be performed for continuous optimisation of network structure and model parameters until an optimal solution is obtained and testing accuracy is satisfied to ensure that generalisation error is minimised from the seen data perspective, and can be further optimised when unseen data is available or real-time streaming data processing will continue for Coherence Learning to converge to the optimised networks, which can provide creative solutions for very complex problems and will spur innovations that are well positioned to lead AI-based data-driven transformation for accelerating progress towards net zero targets.

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Machine learning and artificial intelligence have become the driving force for major innovations across different industries particularly telecommunication evolution from 5G to 6G. 6G network as a sensor will enable joint communication, sensing and localization that will address the needs of industries with a single system, thereby reducing cost. 6G will contain elements that are a natural extension of 5G and 5G-Advanced and will in some ways build on the new business requirements such as driverless vehicles/drones by adopting innovative technologies that can make 6G networks smarter and more efficient to cope with a much larger volume of data and help business become more data driven.

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To inspire smart factory, Coherence Learning allows both knowledge-based and data-driven AI techniques to be applied to correct errors and perform predictive control and maintenance to prevent failure without unnecessary interruptions, improving energy efficiency simultaneously. By using the new algorithms and input-output models to enhance productivity and efficiency, and enhance decision-making and performance, real-time coherence learning can help create insights that provide visibility, predictability and automation of operations, and can be applied to produce technology innovations to power products and services that benefit from the digital transformation of Industry 4.0/5.0.

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Automation and Communication Systems

Research on 6G and IoE has now been focused on wireless channel measurements, characteristics, and models for real-time data processing for automation systems, satellite communications, unmanned aerial vehicle (UAV), high-speed train (HST), vehicle-to-vehicle (V2V), massive multiple-input multiple output (MIMO) systems, and industry IoT and IoE communication channels.

Healthcare Applications

Medical imaging is essential in the healthcare industry as it enables better and more accurate clinical diagnosis, treatment and risk prediction. Common medical images involve X-ray, Computed Tomography (CT), ultrasound and Magnetic Resonance Imaging (MRI). Coherence Learning has the capabitility to reduce the speed constraint on image acquisition and reconstruction, which has the potential to achieve fast and real-time at-the-scanner processing with accelerated acquisition.

Professional Training

The training programs will be delivered with focus on both theory and application, and particularly exploring how to design and build AI products and services, which will provide you with in-depth knowledge of fundamental and advanced concepts and trends in AI, the ability to determine when machine learning is feasible and can add value to business challenges, and the capacity to leverage the power of data and evaluate machine learning methods to improve performance.

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