Deep Learning - ELEC96033
Aims
This is a continuation of the Autumn term course EE3-35 Machine Learning. In contrast to machine learning included in EE3-23, EE3-25 deep learning will focus on deep neural network based learning. It introduces the background and illustrates how deep learning is impacting our understanding of intelligence and contributing to the practical design of intelligent machines.
Deep learning is currently the most active area of research and development and in high demand for experts by hi-tech start-ups, large companies as well as academia. It is the preferred approach for modern AI and machine learning in any domain. Deep learning techniques enable us to automatically extract features from data so as to solve predictive tasks, such as speech recognition, object recognition, machine translation, question-answering, anomaly detection, medical diagnosis and prognosis, automatic algorithm configuration, personalisation, robot control, time series forecasting, and much more.
Upon completion of this module, the student will be able to demonstrate and apply knowledge and understanding of:
- The underlying mathematical and algorithmic principles of deep learning
A wide variety of deep learning algorithms
- The key factors that have made deep learning successful for various applications
How deep learning fits within the context of other ML approaches and what learning tasks it is or isn’t suited for
- How to perform evaluation of deep learning algorithms and model selection.
What is involved in learning from data.
- The challenges of deep learning
- The problems that arise when dealing with very small and very big data sets, and how to solve them.
Role
Lecturer
Machine Learning - ELEC96031
Role
Lecturer
Pattern Recognition - EE9-SO29
Role
Lecturer
Computer Vision and Pattern Recognition - ELEC97112
Role
Lecturer