Oxford, United Kingdom

Artificial Intelligence and Machine Learning: Theory and Practice

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 begin with an introduction to the basics of programming in Python, in particular understanding object-oriented programming and its importance to deep learning. You will quickly proceed to an introduction to artificial intelligence, examining the fundamentals of supervised machine learning, including linear regression, logistic regression, neural networks, and gradient descent. In the second week of the course you will explore image processing, investigating transformations, convolutional filters, and edge detection, before an introduction to convolutional neural networks and some prominent CNN architectures such as VGG and ResNet. In the final part of the course, you will look at the core concepts of natural language processing, including sequence modeling, autoregressive models, and recurrent neural networks.

This intensive course offers both a theoretical introduction to artificial intelligence and machine learning concepts, and an opportunity to put this knowledge into action in solving small-scale practical problems from diverse domains.

Target group

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 experience of artificial intelligence, machine learning, or the Python programming language is not required.

Course aim

By the end of this course, you will:
- Understand theoretical concepts of artificial intelligence and machine learning.
- Know how basic artificial intelligence and machine learning tools are used in practice.
- Know how to implement basic algorithms and train small networks for practical problems.
- Be able to identify and use relevant artificial intelligence and machine learning tools in research.
- Know how to implement and deploy artificial intelligence and machine learning algorithms on 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.

Fee info

GBP 1360: This includes:
- All tuition, including lectures, seminars, and tutorials.
- Assessment, transcript of academic performance, and certificate.
- Access to the LMH Summer Programmes remote learning platform.
- Support of the dedicated Remote Learning Coordinator.