Enhance your knowledge and expertise in computer science or electronics, and address the challenges in industry where machine learning techniques are being used increasingly in a wide number of applications. This course is supported by Intel's AI Academy through their online and data processing resources.
Artificial intelligence and machine learning is a growing industry worldwide. Societies are adapting to the new technology landscape, becoming more flexible and also inheriting an attitude of lifelong learning, collaboration, innovation and entrepreneurship.
Using a range of skills from data science, to programming and hardware architectures that allow suitable artificial intelligence solutions to be produced and implemented, this course focuses on up-to-date theoretical and practical developments within machine learning, neural networks, signal processing and remote sensing and how these occur in the intelligent systems. It also allows you to become acquainted with current developments in artificial intelligence, and be able to apply yours skills in intelligent system design and development.
You will be trained in subjects that address the challenges of the current industry, studying modules that focus on data acquisition technologies and data processing techniques, including the development of AI systems, allowing you to become acquainted with digital signal processing, remote sensing and Internet of Things platforms, learning to program processors produced by ARM Ltd, a major player in the world of microelectronic component software/hardware design, based in Cambridge.
You will also explore neural networks and artificial networks, deep learning in Python using Scikit-learn, machine learning models and model evaluation using performance matrices, parametric and non-parametric algorithms or decision trees. Develop fundamental methods and algorithms that enable intelligent systems to interact with their environment through feedback, autonomously learn from data, and interconnect with each other to form collaborative networks, turning mathematical and theoretical insight into enhanced autonomy and performance of real-world physical systems. The practical skills gained help to prepare you for jobs related to intelligent systems and machine learning.