This MSc Programme teaches how to engineer the machine learning systems that will form the basis of our economies, society and industry in the next few decades.
To find out core information about this degree, such as entry requirements, programme length and cost, visit the UCL prospectus site.
Students undertake the following compulsory modules:
- Applied Machine Learning Systems I (MLS-1) (ELEC0134)
- Applied Machine Learning Systems II (MLS-II) (ELEC0135)
- Data Acquisition and Processing Systems (DPS) (ELEC0136)
- Security and Privacy (ELEC0138)
- Cloud, Data Centres and Edge-Computing (ELEC0137)
- Emerging Topics in Integrated Machine Learning Systems (ELEC0139)
- Module descriptions
Applied Machine Learning Systems II (AMLS-II)
This module will cover advanced principles and practice of machine learning systems engineering, including deep learning, deep reinforcement learning, generative adversarial networks, and future directions in machine learning engineering. The module will also encompass Lab sessions – based on programming languages/platforms such as Python or R or tensorflow – so that students can learn how to apply machine learning technology to address various advanced machine learning tasks.
Data Acquisition and Processing Systems (DPS)
This module will cover technology, principles and applications of signal acquisition, compression, and processing systems. In particular, the module will cover a wide range of topics such as sampling theory and practice, analogue-to-digital and digital-to-analogue converters technology, compressive sensing, signal processing principles, image and video processing, and Hardware architectures for data processing.
The module will also encompass lectures as well as lab sessions so that students can learn how to apply the underlying principles to address problems in the area of signal, image and video acquisition, compression, analysis and processing.
Cloud, Data Centres and Edge-Computing
Data centres and edge-computing form the backbone of Cloud systems. Data centres can grow to warehouse-scale computers formed of hundreds of thousands of servers running most of our online services and applications. Meanwhile edge-computing is aiming at performing data processing at the edge of the networks, near the source of the data to support latency sensitive applications and supports a wide range of technologies.
This module will provide fundamental technical details on how to design a Data Centre network from switch technologies and architectures, to network protocols, topologies and interconnects while delving into advance solutions for future systems. Furthermore, the module will use technical research papers from recent years in top venues to the topics of edge computing as well as hands-on python based labs to simulate edge-computing systems.
Security and Privacy (SP)
This module will cover the design, the development and the evaluation of secure computer systems & networks. In particular, it will be focused on security/privacy challenges in a "Big Data" world.
It will also pay particular attention to new and emerging technologies, such as Blockchains and Distributed Ledger Technologies, which hold significant promise in terms of security and privacy. The module will also encompass Lab sessions so that students can learn how to implement/test secure networks.
Seminar in ‘Emerging Topics in Integrated Machine Learning Systems’
This module consists of a series of weekly seminars delivered by high-profile academics, industrialists, or other stakeholders in machine learning technology to expose students to the most cutting-edge topics in the field, including recent advances in machine learning theory, algorithms, and applications, as well as issues such as privacy, fairness and ethics in artificial intelligence. Each student will be required to maintain an up-to-date blog reporting the state-of-the-art in key topics.
Students undertake two out of the three following optional modules:
Optional modules are also complemented by a compulsory non-credit bearing Professional Development Skills module that will deliver students research, writing, and presentation skills.
All students undertake an independent research project which culminates in a dissertation of approximately 12,000 words.
All students are also due to make a presentation mid-way through their independent research project.