Machine Learning for Data Science (CEGE0004)

Key information

Faculty: Faculty of Engineering Sciences
Teaching department: Civil, Environmental and Geomatic Engineering
Credit value: 15

Restrictions: Geospatial Programming (CEGE0096) or an equivalent module where Python is taught.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

This module covers principles of machine learning (ML) and a general overview of AI technology and terminology. This module will make students gain intuitions about a number of ML algorithms and make them comfortable in applying them to new data and tasks. Also, students will gain experience in developing (in python) ML-based solutions to real world problems.

Learning outcomes:

To understand the basic principles of machine learning;
To be comfortable in applying appropriate machine learning methods to new data and tasks;
To understand the fundamentals of algorithms in machine learning;
To understand the fundamentals of algorithms for deep learning;
To have a general overview of the current AI technology and terminology.

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% Other form of assessment
Mark scheme: Numeric Marks

Other information

Number of students on module in previous year: 34
Module leader: Dr Aldo Lipani
Who to contact for more information: aldo.lipani@ucl.ac.uk

Last updated

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

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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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