Overview

This practical short course provides and introduction to the basic principles of machine learning systems engineering.

You'll learn about:

• supervised and unsupervised learning algorithms
• kernel learning
• neural networks

You'll also learn how to apply machine learning technology to address various data science problems through a series of hands-on programming sessions. In these you'll use programming languages/platforms including Python and Tensorflow.

Who this course is for

This course is aimed at those working in the information and communications technology (ICT) industry such as researchers, engineers, IT professionals and managers.

It's particularly suited to graduates in engineering and computer science 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, however you should know the basics of calculus, algebra, statistics and programming.

Course content

Topics covered include:

• Introduction to machine learning 
  • Historical perspective
  • Supervised vs unsupervised learning
  • Deterministic vs probabilistic models
  • Classification and regression problems
• Supervised learning
  • Basic regression methods
  • Basic classification methods
  • Decision trees for regression and classification
  • Ensemble methods
  • Parameter tuning
  • Use-cases
• Unsupervised learning
  • Clustering (k-means, EM, spectral clustering)
  • Use-cases
• Kernel learning
  • Creating non-linear algorithms by “kernelization”
  • Support vector machines for classification and regression
  • Use-cases
• Introduction to neural networks
  • Neural network architectures
  • Neural network training
  • Use-cases

Dates, assessment and certificates

Classes will be held from 2 - 5pm on four Tuesdays, 2 - 4pm on three Tuesdays and 9-11am on one Monday. 

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

The course is assessed through a programming assignment.

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

If you take and pass the assignment you'll get a certificate stating this, which includes your pass level. You'll be able to use this towards a flexible Master's degree.

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 the telecommunications industry 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 be able to:

• appreciate the major technological developments in machine learning engineering, including its history to date
• understand general and specific ideas, methodologies, and algorithms in machine learning engineering, including supervised learning, unsupervised learning, and various learning algorithms
• understand how to apply machine learning techniques to solve particular real-world problems via a number of use-cases

Course team

Professor Miguel Rodrigues

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

Professor Yiannis Andreopoulos

Yiannis is Professor of Data and Signal Processing Systems in the Electronic and Electrical Engineering Department at UCL. His research interests include signal processing and deep neural networks, error-tolerant computing, multimedia systems (mainly video), and wireless protocols for low-end systems (e.g. sensor networks).

Course information last modified: 22 Sep 2023, 23:41