Short courses


Integrated Machine Learning Systems: Emerging Topics

  • 150 hours
  • 9 weeks (2 hour seminar each Monday) + optional assignment


On this course you'll learn about cutting-edge topics in machine learning technology, including:

  • recent advances in machine learning theory, algorithms and applications
  • issues such as privacy, fairness and ethics in artificial intelligence

Weekly seminars will be delivered by high-profile academics, experts from industry and other stakeholders in machine learning technology.

During the course you'll maintain an up-to-date blog on a chosen topic.

This course is run by UCL's Department of Electronic and Electrical Engineering.

Who this course is for

The department's courses/CPD modules are aimed at researchers, engineers, IT professionals and managers working in various industries.

They're particularly suited to graduates in electronic and electrical engineering, computer science and mathematics who want to further their knowledge on a particular topic, or work towards a Master's degree. 

You don't need to have any pre-requisite qualifications to take this course.

Course content

Representative topics that will be covered include:

  • Beyond von Neumann architectures for machine learning
  • Machine learning advances in speech recognition, translation
  • Machine learning advances in natural language processing
  • Machine learning advances in computer vision
  • Machine learning in healthcare
  • Machine learning in fintech
  • Ethics of artificial intelligence
  • Artificial intelligence infrastructure

Dates, assessment and certificates

Classes will be 2 hours long and held on Mondays, for 9 weeks.

Teaching will take place in person with some materials available online.

You'll be assessed on the quality, comprehensiveness and timeliness of the contents of a blog on advances in a chosen topic, which you'll update throughout the course.

If you complete the course but not the assessment, you'll receive a certificate of attendance.

If you complete and pass the assessment, you'll get a certificate stating this, which includes your pass level.

Benefits of UCL's Electronics and Electrical Engineering CPD courses

You can take this course as a standalone (one-off) course/module, or accumulate it towards a Master's degree (up to two standalone modules can be transferred towards the flexible Master's degree).

Benefits to the employee
The programme offers the opportunity for professional people working in various industries to develop their career, be able to respond to changes in their environment, and learn while they earn. It's also designed to give you the opportunity of working towards an MSc qualification from an academic institution whose quality is recognised world-wide.

Benefits to employers
Our flexible CPD courses enhance staff motivation and assists in the recruitment and retention of high-quality staff. It enables your company to keep ahead of the competition by tapping into world-leading research, and to profit from UCL's world class Telecommunications and Business expertise.

View the full range of related courses available.

Learning outcomes

On completion of this course, you should:

  • appreciate the most cutting-edge advances in integrated machine learning systems engineering, including its applications in technology, business, economy and society
  • understand general and specific approaches to integrated machine learning systems engineering
  • understand how to apply integrated machine learning systems approaches to solve particular real-world problems via a number of use-cases
  • appreciate future technological trends and applications of integrated machine learning systems engineering
  • understand how to communicate recent trends in integrated machine learning systems engineering to generalist and specialist audiences by maintaining an up-to-date blog

Course team

Professor Miguel Rodrigues - course leader

Professor Miguel Rodrigues - course leader

Miguel's areas of research interest include information theory, information processing, and machine learning.

Course information last modified: 22 Sep 2023, 17:05