About this subject
In our age of burgeoning smart technology and automation we are already seeing the transformative potential of Artificial Intelligence and Machine Learning in fields as diverse as finance, medicine, and manufacturing. This subject offers a hands-on introduction to this future-focused area of research.
You will be introduced to the Python programming language and the theoretical foundations of the key concepts in Artificial Intelligence and Machine Learning, before embarking on linear regressions and tackling loss function, regularization techniques, and bias-variance trade-off. You will explore and implement stochastic gradients descent for regression using TensorFlow and PyTorch. The course progresses from simple Neural Networks to Convolutional Neural Networks and the implementation of MNIST classification. By the end of the course large-scale problems of semantic segmentation, edge detection and metric learning will be implemented on AWS/Google Cloud. Throughout the course you will solve practical problems of Artificial Intelligence and Machine Learning from diverse domains.
After studying this subject you will:
- Understand the theory of machine learning and artificial intelligence.
- Know about Artificial Intelligence and Machine Learning tools used in practice.
- Know how to implement basic algorithms of Artificial Intelligence and Machine Learning and train small networks for practical problems.
- Be able to identify and use relevant Artificial Intelligence and Machine Learning tools in their research.
- Know how to implement and deploy Artificial Intelligence and Machine Learning algorithms on AWS/Google Cloud.
Who is this subject suitable for?
This subject would suit STEM students in undergraduate or entry-level postgraduate study. Basic knowledge of calculus and linear algebra is required, and some experience of coding is recommended. Prior knowledge of Artificial Intelligence, Machine Learning, or the Python programming language is not required.
Dr Naeemullah Khan is a Research Fellow at Lady Margaret Hall and a Postdoctoral Research Scientist at the Department of Engineering, University of Oxford.
Dates and availability
This course is available as a residential programme:
7th August 2022 to 27th August 2022
This course is available as an online programme:
8th August 2022 to 26th August 2022