In our age of burgeoning intelligent 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 course offers a hands-on introduction to this future-focused area of research.
You will be introduced to the Python programming language and the theoretical underpinnings of the key concepts of Artificial Intelligence and Machine Learning, before undertaking linear regressions and tackling the loss function, regularization techniques, and the bias-variance trade-off
You will explore and implement stochastic gradient descent for regression using TensorFlow and PyTorch
The course proceeds from simple neural networks to convolutional neural networks and the implementation of the MNIST classification
By the end of the course on AWS / Google Cloud large-scale problems of semantic segmentation, edge detection and metric learning will be implemented
During the course you will solve practical problems of Artificial Intelligence and Machine Learning from different domains.
Course leader
Dr Naeemullah Khan is a research fellow at Lady Margaret Hall and a postdoctoral fellow in the Department of Engineering at the University of Oxford.
Target group
This course is suitable for STEM students in entry-level undergraduate or postgraduate studies
Basic knowledge of calculus and linear algebra is required and some programming experience is recommended
No previous knowledge of Artificial Intelligence, Machine Learning or the Python programming language is required.
Goal of the course
After studying this course:
• You will understand the theory of machine learning and artificial intelligence.
• Know the Artificial Intelligence and Machine Learning tools used in practice.
• Knowing how to implement basic algorithms of Artificial Intelligence and Machine Learning and form small networks for practical problems.
• Be able to identify and use relevant Artificial Intelligence and Machine Learning tools in their research.
• Knowing how to implement and distribute Artificial Intelligence and Machine Learning algorithms on AWS / Google Cloud.
This course is available as a residential program:
From 7 August 2022 to 27 August 2022
This course is available as an online program:
From 8 August 2022 to 26 August 2022
First round application deadline: 10 June 2022 (applications received after this date will be processed according to availability).
Start: 07/08/2022
End: 27/08/2022
Map: Lady Margaret Hall, University of Oxford, Norham Gardens, Oxford OX2 6QA,UK
Email: [email protected]
