COM6033 - Applied Artificial Intelligence

Objectives:

On successful completion of the module, students will be able to:
Demonstrate detailed and accurate understanding of intelligence and intelligent systems, use of intelligence for machine learning, ethics and current AI regulations;
Demonstrate understanding of basic and advanced machine learning algorithms and applications;
Demonstrate an ability to collect, clean and prepare datasets in a machine-readable format;
Train, test, evaluate and optimise machine learning models and apply them to real-world problems.

Content:

Applied artificial intelligence module covers the main elements of intelligence and intelligent systems with a focus on machine learning. It explores the use of intelligence in machine learning, ethics and AI regulations. This module explores various machine learning algorithms and techniques including simple statistical supervised/unsupervised algorithms such as regression/clustering to advanced algorithms such as reinforcement learning, deep learning and deep reinforcement learning. The module also explores the applications of machine learning models in different domains such as medical diagnosis. Students collect datasets for a real-world problem; clean and pre-process data for training AI models; test and evaluate the performance of the models; and optimise and fine-tune parameters to enhance the performance of the models. Students also explore the current AI cloud services, tools and advanced technology such as Azure or Google to build, train and deploy machine learning models in various business domains.

Learning and Teaching Information:

Workshop sessions provide the opportunity for students to work through a range of artificial intelligence case studies, using a range of technologies and techniques. Data and AI implementations in real-world problems are used to reflect practices by designing, building, training, testing and evaluating AI solutions.

Workshops
Hours: 40
Intended Group Size: Cohort

Guided independent study
Hours: 260

Further details relating to assessment
Project: This project artefact produces a machine learning model that is trained, tested and evaluated using a given real-world dataset.

Guidance Report: Through this report students produce guidance for a specific industry such as education, medical or engineering on what they need to consider when developing an AI system. Students will consider the legal, ethical and moral aspects when designing, constructing and evaluating the AI/ML systems from the human and machine contexts. This report will require students to reflect on all preceding lectures and activities.

In this module, formative assessment will be used to support the skills that contribute to the assessment. Formative assessment may include coding labs, design and modelling tasks, case study presentations, short quizzes, or specific research tasks. Formative feedback will be an ongoing process within class sessions.

Full details are available in the Module Handbook.

For students studying at Leeds Trinity campus, there are variations to the Taught Programme Academic Regulations, as required by the accrediting body, and these variations are contained within Additional Regulations.

Each assessment component must be passed, in accordance with PSRB requirements.

Assessment:

001 Project; Artefact; 2,000 words equivalent; end of semester 1 50%
002 Guidance Report; 2,000 words; end of semester 1 50%

Fact File

Module Coordinator - Antesar Shabut
Level - 6
Credit Value - 30
Pre-Requisites - PROBABILITY - 2 WEEKS OF WORKSHOPS TO COVER BASIC CONCEPTS OF PROBABILITY FOR COMPUTER SCIENTISTS
Semester(s) Offered - 6S1

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