Overview

This practical short course covers advanced principles and practice of machine learning systems engineering, including:

• deep learning
• deep reinforcement learning
• generative adversarial networks
• future directions in machine learning engineering

You'll learn how to apply machine learning technology to address various advanced machine learning tasks in lab session. These sessions will be based on programming languages/platforms such as Python, R or tensorflow.

Who this course is for

This course is for researchers, engineers, IT professionals and managers working in various industries.

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

Prerequisites

Before you take this course, you must have completed our introductory course on applied machine learning systems (see the UCL short courses pages for more details).

Course content

Topics covered include:

• Deep neural networks 
• Overview of classification and deep neural networks 
• Convolutional neural networks, recurrent neural networks, and LSTMs (long short-term memory networks)
• Training deep neural networks, gating architectures and use cases
• Reinforcement learning (RL)
• Introduction to RL and how to cast problems into RL
• Exploration vs exploitation
• Practical solving methods and tricks to improve the learning methods
• Challenges in DeepRL and use-cases
• Adversarial learning 
• Generative adversarial networks 
• Discriminative adversarial networks
• Semi-supervised learning and use cases   
• Bayesian frameworks 
• Learning based on priors
• Comparisons/applications versus frequentist frameworks
• Deep feature extraction and similarity with applications to text processing 
• Natural language processing, applications and emerging frameworks