Artificial Intelligence and Data Science BSc Honours

You will gain an understanding of how to develop programs in Python.  Working with standard interactive development environments, you will design, implement, debug, and evaluate software. 

You will develop a broad understanding of the intersecting domains of Artificial Intelligence and Data Science including the design, development, evaluation and deployment of models. You will become familiar with typical Data Science workflows and the fundamental ideas of Artificial Intelligence.

You will be introduced to the concepts and notation of data structures and algorithms and their use in analysing problems and organising solutions. You will be equipped with a range of methods to approach problems, to enable you to manipulate and analyse data in practical settings.

This module teaches you the fundamental knowledge and principles computer science theory, such as discrete mathematics, propositional logic and graph theory. You will study the existing fundamental algorithms that are ubiquitous across computer networking and programming, gaining insights into their application in real-world computing systems.  

In this module you will gain practical experience developing dynamic websites using HTML, CSS and JavaScript. You'll learn how to store client-side data and get first-hand experience of web services and the server-side data storage, considering the needs of a wide and diverse range of stakeholders. 

Modern businesses require making data-driven decisions requiring thoughtful data analysis. You will gain the foundational understanding and skills for data analysis in an enterprise setting. This includes a range of techniques for retrieving, organising, analysing and visualizing data as well as running simulations and training data-driven models to generate forecasts necessary for business decisions. Special consideration will be given to the analysis of multidimensional data in the broad context of entire organisations.

You will learn about several important topics in ML and AI that remain critical for solving diverse real-world problems.  These include learning concepts and associative rules from data, Markov decision processes, reinforcement learning and randomized or evolutionary algorithms.  The module will also emphasize the notion of incomplete information, which characterizes learning and distinguishes it from ordinary optimization.  

You will develop a theoretical understanding of the architecture of large language models (LLMs) and how they are trained. You will gain practical experience with the customization and use of LLMs for different applications including prompt engineering, fine tuning, and the use of retrieval augmented generation (RAG) to control the output of a LLM.

The module aims to develop your employability skills by achieving the set of agreed learning outcomes using a Three Way Negotiated Learning Agreement. The module develops a range of skills specific to the individual workplace.

This practical experience module provides the means for you to link academic work with the 'real world', facilitating the embedding of transferable and graduate skills necessary for future career paths and employment.

You will reflect upon areas of knowledge relevant to the placement learning experience and develop personal knowledge through a review of your learning, with the opportunity to enhance your self-expression, communication, self-reliance and co-operation.

You will round out your knowledge of basic Artificial Intelligence (AI), including long standing areas, such as rule based systems, search spaces and constraint satisfaction, and emerging technologies. You will strengthen your ability to understand technologies as they emerge.

You will demonstrate how effectively you have consolidated your skills, knowledge, and experience from other modules by means of an individual project. The project must incorporate a solution to an AI or Data Science problem or to a relevant theoretical research problem. 

You will be introduced to a variety of new and commonly used biologically inspired algorithms and techniques so as to be able to solve a range of problems in AI and Data Science, including gradient-based algorithms, genetic algorithms, differential evolution, particle swarm optimization, firefly algorithm and other evolutionary algorithms. 

You will gain the knowledge and practical Artificial Intelligence and Machine Learning skills used in robotics. You will cover different types of autonomous robots in a variety of fields and applications. You will acquire the knowledge and practical skills of robot sensory processing, particularly vision, and the use of machine learning methods and algorithms, and how these are applied to real life autonomous robotic applications.

You will study different types of network and cyber threats to computer systems and networks, and learn the various measures needed to secure systems to counteract and mitigate against these threats. 

You will gain appreciation and an understanding of the rigorous mathematics that plays a vital role in Data Science and AI. You will gain the necessary background to access and contribute to technical research in the fields of AI and Data Science.

You will work with simulated spiking neural networks and networks emulated on neuromorphic computers to understand how brains work and use efficient parallel computation. You will develop novel neural models, agents and machine learning systems that are at the leading edge of the field.

You will be studying at our leafy north London campus in Hendon.

Outside of teaching hours, you’ll learn independently through self-study which will involve reading articles and books, working on projects, undertaking research, and preparing for assessments including coursework, presentations and exams.

Your course timetable will balance your study commitments on campus with time for work, life commitments and independent study.

We aim to make timetables available to students at least 2 weeks before the start of term. Some weeks are different due to how we schedule classes and arrange on-campus sessions.

Our excellent teaching and support teams will help you develop the skills relevant to your degree from research and practical skills to critical thinking. Our Sheppard Library is open from 7am to 11pm Monday to Sunday during term time. And we offer free 24-hour laptop loans with full desktop software, free printing and Wi-Fi to use on or off campus, even over the weekend.

This course is mainly assessed through coursework including exams, tests, written assignments, presentations and group exercises. The exact balance will depend on the modules you are taking. 

Summative assessment is largely based around the development of artefacts, typically software. Other forms of assessment include reports, in class assessments, portfolios, homework style questions, essays and demonstrations.

Laboratory sessions are mostly devoted to formative assessment including course work support. A group project in the third year supports the development of complex systems by several collaborating students. The final project is a dissertation typically based on an artefact the student has developed, and evaluation includes a viva with two academics.

Feedback is given informally throughout the programme via conversation and formally via the virtual learning environment.