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Module Title
LM Data-intensive Artificial Intelligence
School
Computer Science
Department
Computer Science
Module Code
06 41897
Module Lead
Paolo Missier
Level
Masters Level
Credits
20
Semester
Semester 1
Pre-requisites
Co-requisites
Restrictions
None
Contact Hours
Lecture-15 hours
Tutorial-7 hours
Supervised time in studio/workshop-11 hours
Guided independent study-167 hours Total: 200 hours
Exclusions
Description
This module provides an overview of the technology and core algorithms used to collect, select, engineer, evaluate and deploy large datasets that are to be used for training Machine Learning and AI models. Dedicated solutions are discussed for different types of data. The module includes hands on experience with tools and programming. It also introduces methods for managing the fairness of Machine Learning and AI models, and covers ethical issues around the application of AI.
Learning Outcomes
By the end of the module students should be able to:
Design and scale AI and data processing pipelines, including data collection, preprocessing, feature engineering, model training, evaluation, monitoring and retraining
Systematically apply AI pipelines and data processing frameworks using programming and existing tools
Critically evaluate AI and data processing pipelines
Understand the challenges that emerge when applying AI in practice, including the challenges associated with processing different types of big data
Demonstrate critical awareness of the ethical and fairness issues around the application of AI
Assessment
Assessment Methods & Exceptions
Assessment:
Written unseen examination (80%); continuous assessment (20%)
