Intelligent Systems: An Introduction to Deep Learning and Autonomous Systems

Delve into the inner workings of deep learning

From Ada Lovelace until the first decade of this century, we have relied on expert computer programmers to design and write software. Now, a whole new branch of computer science called machine learning is allowing computers to create their own software by learning from data.

On this three-week course from the University of York, you’ll discover the fundamental theory and techniques behind deep learning as well as how it’s used in applications.

Explore machine learning applications and the uses of deep learning

Deep learning is a form of machine learning that has provided performance breakthroughs across a whole host of areas.

From household devices to image processing, you’ll dive into the different areas that currently use deep learning as well as looking at how it works and whether we should worry about machines taking over the world.

Assess the safety and ethics surrounding machine learning

With machine learning giving rise to autonomous systems such as self-driving cars, there are many questions about putting our safety in the hands of these machines.

On this course, you’ll consider the ethical implications of machine learning, such as learning from personal or biased data, and of trusting your safety to a learnt system that no human can understand.

Learn from the experts at the University of York

The Department of Computer Science at the University of York is home to world-leading expertise in computer vision, and to the Assuring Autonomy International Programme, at the leading edge of assuring the safety of autonomous systems through machine learning.

With the help and guidance of top educators from the University of York, you’ll explore the main differences between machine learning and conventional programming and how machine learning is evolving autonomous systems.

This course is designed for anyone interested in machine learning and looking to further their understanding of recent innovations and research in the area.

It will be especially useful if you are looking to apply to a related undergraduate programme in the near future.

To fully engage with the materials we recommend you have at least some experience of A-Level Maths (or equivalent).