This module will cover the fundamental concepts and techniques from linear algebra, differential calculus and probability.
The acquired tools will help the student to understand how AI is built and enable them to tailor standard methods to specific problems they will be facing and to interpret the results obtained through data analysis.
This module offers a comprehensive introduction to programming, focusing on Python for students with varying backgrounds. Beginning with fundamental programming concepts, the course progresses to explore data visualisation techniques and key libraries. Emphasis is placed on hands-on learning, enabling students to develop practical skills in data exploration and analysis. By the end of the course, students gain proficiency in programming, data visualisation, and problem-solving, preparing them to apply these skills across diverse fields and disciplines. Assessment method: 100% coursework.
The module introduces you to the basic theory, concepts, and techniques of machine learning using Python. It will cover the main topics and essential theory in the area. The module also focuses on developing practical skills in designing and developing machine learning systems using suitable software and algorithms in order to solve real-world problems.
The module introduces neural networks and deep learning, one of the key topics in modern applied AI.The module will provide the student with an in-depth understanding of all the components of neural networks, from the different computational building blocks to the functions to be optimized and finally to the different optimization strategies.The module will present how neural networks can be trained and validated to learn from data to solve specific tasks, and how they evolved into deep neural networks, convolutional neural networks and graph networks, presenting their major architecture concepts.
The module covers theoretical and practical aspects of Natural Language Processing to design, develop and implement Collaborative and Cognitive Communication Systems for robotics while linking AI and NLP to human psychology patterns and applying it to Chabot’s, digital assistants and context-aware personalized interfaces. The lecture sessions will deliver state-of-the-art theories, models and algorithms covering neural Language models, speech tagging, Vector Semantics and Embedding, information extraction and sentimental analysis. The lab sessions will deliver hands-on practical sessions using Chabot frameworks, Python NLP libraries and tool kits for NLP.
The module introduces you to the basic theory, concepts, and techniques of data mining, and its role in data science and business intelligence. It will cover the main topics in the area. The module also focuses on developing practical skills in solving real-world data mining problems by using appropriate software suites. Base SAS®, SAS® Enterprise Miner, SAS® Enterprise Guide and Tableau® may be taught and used for this purpose.
The module will provide students with the critical skills, knowledge and analytical abilities needed to identify and address ethical challenges as they arise in practice from the application of AI. The module will engage with the ethical and societal challenges of AI and is thoroughly informed by the knowledge, theories and methods of established academic disciplines from philosophy to computer science.Industrial seminars on AI applications will complement the content of the module, exposing the students to the challenges of developing an AI application, considering the short- and long-ranging societal fallout.
The module requires students to undertake an independent piece of research/development work, investigating in depth a subject, in which they have a particular interest and of their own selection. The dissertation assesses students’ ability to integrate information from various sources, to conduct an in-depth investigation, where necessary specify, appropriately develop bespoke software/technology-based solutions, to critically analyse results and information obtained and to propose improvements/further work. Each student will submit a dissertation of between 12,000-15,000 words.
The module requires students to undertake an independent piece of research/development work, investigating in depth a subject, in which they have a particular interest and of their own selection. The dissertation assesses students’ ability to integrate information from various sources, to conduct an in-depth investigation, where necessary specify, appropriately develop bespoke software/technology-based solutions, to critically analyse results and information obtained and to propose improvements/further work. Each student will submit a dissertation of between 12,000-15,000 words.
