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 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 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.
Dr Naeemullah Khan is a Research Fellow at Lady Margaret Hall and a Postdoctoral Research Scientist at the Department of Engineering, University of Oxford.
This course 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.
After studying this course 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.
Credits info: 7.5 EC
LMH Summer Programmes are designed to be eligible for credit, and we recommend the award of 7.5 ECTS / 4 US / 15 CATS for this course.
GBP 3500: The Programme Fee includes:
- All tuition, including lectures, seminars, and tutorials.
- Assessment, transcript of academic performance, and certificate.
- A co-curricular programme of skills workshops and guest speakers.
- Access to the Lady Margaret Hall College Library.
- Bed & Breakfast accommodation throughout your programme.
- Lunch and dinner in the College Dining Hall Monday to Friday.
- A full Social & Cultural Programme, including two excursions to other English cities.
- A high-quality printed class photograph.
- Formal Graduation banquet.
For more information click "LINK TO ORIGINAL" below.