Applied Machine Learning Systems II (MLS-II) (ELEC0135)

Key information

Faculty: Faculty of Engineering Sciences
Teaching department: Electronic and Electrical Engineering
Credit value: 15

Restrictions: Only available to TMSIMLSSYS01, CPD and 果冻影院 Short Courses.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

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 鈥� based on programming
languages/platforms such as Python or R or tensorflow 鈥� so that students can learn how to apply machine
learning technology to address various advanced machine learning tasks.

Module deliveries for 2024/25 academic year

Intended teaching term: Term 2 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study: In person
Methods of assessment: 100% Coursework
Mark scheme: Numeric Marks

Other information

Number of students on module in previous year: 35
Module leader: Ioannis Andreopoulos
Who to contact for more information: eee-msc-admin@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

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Useful links

Sustainability

For a list of modules related to climate change as well as social and environmental sustainability at 果冻影院, type 'climate' or 'sustainability' into the search. Visit Sustainable 果冻影院 for information on extra-curricular activity on sustainability.

Important information

The catalogue has been updated with key information about the modules that will run during the 2024/25 academic session. Current and prospective students can browse through the catalogue to consider possible module choices for the coming year.

Please note,听information in the catalogue is听subject to change as teaching and assessment arrangements for 2024/25 may need to be adjusted in line with the University's Feedback and Assessment principles and operating model.

Centrally managed exam durations will be provided on students' individual exam timetables. Arrangements for locally managed exams and in-class activity will be confirmed by the teaching department for the module.

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