COM6203 - Applied Artificial Intelligence

Objectives:

Content:

By engaging in this module, students will develop proficiency in navigating the intricate landscape of AI, enabling them to craft intelligent predictive solutions tailored to the needs of businesses striving for sustainability. The emphasis on practical applications equips students with a robust skill set that goes beyond theoretical knowledge, fostering a capacity to implement AI solutions in real-world scenarios. The curriculum provides a hands-on learning experience, exposing students to a diverse array of algorithms. From fundamental techniques such as linear regression and decision trees to more advanced methods such as neural networks, students will gain a nuanced understanding of how these algorithms operate. Through practical exercises, they will learn to transform raw data into actionable insights, predict outcomes and uncover concealed patterns. The utilisation of industry-standard tools ensures that students are well-versed in the technologies commonly employed in the field. Moreover, the module delves into concepts of deep learning, enriching students' understanding of cutting-edge AI technologies. This holistic approach equips them with a well-rounded skill set, preparing them to tackle the multifaceted challenges presented by real-world AI applications.

Learning and Teaching Information:

The learning and teaching method for this module is centred on a series of predominantly practical guided activities, initiated by short theoretical introductions. Artificial Intelligence is best learned through practical application, as it involves building and testing models to address specific problems. Students could participate in hands-on projects using industry-standard tools such as Python, TensorFlow and PyTorch, allowing them to apply AI techniques to realistic scenarios and datasets. The goal is to develop the students’ critical perspective while gaining up-to-date domain knowledge through practical work. Each session includes preliminary exercises and readings based on online materials conducted by students on their own. After the workshop, students individually consolidate their understanding through additional activities and further readings.

Workshops
Hours 40
Group Size All Cohort

Guided independent study
Hours: 260

AI Guidance Report: In this report, students generate recommendations for a particular industry, such as education, medical or engineering, outlining crucial considerations in the development of an AI system. The focus is on examining legal, ethical and equality and diversity dimensions throughout the design, construction and evaluation phases of AI/ML systems, considering both human and machine perspectives. The report necessitates students to reflect on the module's content as they provide insights into the comprehensive aspects of creating and assessing AI systems.

AI Artefact: This AI artefact produces a machine learning model that is trained, tested and evaluated using a given real-world dataset.

In computer science classes, formative assessment serves to bolster the skills essential for module success. This includes engaging in practical labs, undertaking design and modelling tasks, delivering study presentations, completing short quizzes and conducting specific investigation tasks. The provision of formative feedback is integrated seamlessly into class sessions, ensuring an ongoing and iterative process to enhance learning outcomes.

Full details are available in the Module Handbook

Assessment:

001 AI guidance report; 1600 word equiv; mid semester 1 40%
002 AI artefact; 2400 work equiv; end of semester 2 60%

Fact File

Module Coordinator - Antesar Shabut
Level - 6
Credit Value - 30
Pre-Requisites - NONE
Semester(s) Offered - 6YL

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