MSc Artificial Intelligence

Previous undergraduate study: 2:2 honours degree and above or equivalent – from a recognised institution.

Other qualifications: overseas qualifications may be accepted and subject to evidence of equivalency normally verified through ECCTIS (UK ENIC).

Work experience and professional qualifications: if you haven't studied at undergraduate level, we welcome applications from anyone who can demonstrate relevant professional experience. In place of work experience, you may be invited to interview or asked to provide a written document to support your application.

Recognition of prior learning: are you considering transferring institutions, or have you studied before? Our recognition of prior learning process unlocks the value of your past learning and experience.

If you’re a non-native English speaker, you’ll be asked to provide proof of your English language proficiency in one of the following forms:

IELTS: level 6.5 or above with no element below 6.0 (an online English test (SPEEXX) is offered if IELTS is not available).

TOEFL iBT®: a score of 88 overall (with 18 in reading, 19 in listening, 19 in speaking and 23 in writing).

Trinity College London Integrated Skills in English (ISE): ISE II with distinction in each skill (please note that Trinity College ISE academic results are only valid for two years).

Duolingo English test: a score of 110 overall, with 100 in each component.

Cambridge Certificate of Proficiency or Cambridge Certificate of Advanced English: a score of 176 overall, with 169 in each component.

Alternatively, you may be accepted for further study if you have previously studied in English at an appropriate level and attended a recognised institution.

You may be eligible for a government master's loan.

As an LIBF student, you’ll have the opportunity to apply for an LIBF Scholarship. The LIBF Scholarship is an award of £1,200 towards your tuition fees, spread over the duration of your course and reducing your total fees. You are eligible to apply if your household income is less than £3,500 per month.

You can get up to 5%* off your total fees if you pay in advance (*reduction depends on study duration). To explore all fees and funding options for your MSc Artificial Intelligence degree, check out our UK and international students fees and funding page or our international students fees and funding page.

Module overview:

enhance your Python knowledge by covering essential syntax, object-oriented programming, and error handling.

Gained skills:

you'll master Python for web development, data analytics, and scientific computing. Learn to write clean, efficient, and adaptable code for different use cases.

Real-world application:

use Python to automate data workflows and build data analysis tools. You can go on to develop predictive models for market trends or create data pipelines to improve inventory management.

Assessment:

You'll complete a 3,000-word written assignment.

Module overview:

deepen your understanding of advanced mathematical techniques, including calculus and matrix theory, which are critical in computer science and needed for data analysis and algorithm development.

Gained skills:

you'll gain proficiency in using mathematical methods to analyse data and make predictions, which are crucial for universal machine learning tools.

Real-world application:

use mathematical principles to optimise algorithms for financial forecasting or improve data processing in scientific research and intelligent systems, especially in areas like evolutionary computation.

Assessment:

You'll take a 90-minute exam consisting of both closed (e.g., multiple choice) and open questions.

Module overview:

learn advanced statistical concepts, search methods and techniques for analysing data and making informed decisions in uncertain environments.

Gained skills:

you'll master probability distributions, Bayesian statistics, and hypothesis testing to evaluate data and optimise models.

Real-world application:

apply advanced statistical methods alongside data science to improve the accuracy of predictive models in business or scientific research.

Assessment:

You'll complete an advanced workbook consisting of six assignments, each 150-300 words.

Module overview:

gain a comprehensive understanding of machine learning, covering supervised, unsupervised, and reinforcement learning techniques.

Gained skills:

you'll learn to select, evaluate, and apply the right machine-learning models for tasks like regression, classification, and clustering.

Real-world application:

Utilise your computer science skills to create artificial neural networks and recurrent neural networks.

Assessment:

You'll take a 90-minute exam consisting of both closed (e.g., multiple choice) and open questions.

Module overview:

Explore advanced research methodologies, including qualitative and quantitative approaches, essential for conducting research and empirical studies.

Gained skills:

Develop skills to design, conduct, and analyse research effectively, ensuring validity and reliability.

Real-world application:

Use these research methods to show learning outcomes, assess business strategies or further learning in corporate settings.

Assessment:

You'll complete a 3,000-word written assignment.

Module overview:

Study the basics of the cognitive science of neural networks and deep learning, including neural networks, architectures and training methods.

Gained skills:

You'll learn to build and train deep learning models for various applications.

Real-world application:

Apply deep learning techniques to create models for image and pattern recognition, natural language processing, and other AI-driven tasks.

Assessment:

You'll complete a 3,000-word written assignment.

Module overview:

Understand the principles of reinforcement learning and computational models, including Markov decision processes and Q-learning.

Gained skills:

You'll learn to apply deep reinforcement learning methods to optimise decision-making in dynamic environments.

Real-world application:

Utilise reinforcement learning and universal machine learning techniques to develop intelligent systems capable of adapting and learning over time, such as automated trading systems and robotics.

Assessment:

You'll complete a 3,000-word written assignment.

Module overview:

Dive into statistical inference and causal analysis, exploring Bayesian networks and probabilistic modelling.

Gained skills:

Using causal inference techniques, you'll learn to evaluate data and interventions in statistical systems.

Real-world application:

Apply data science techniques to inform healthcare, economics, or social sciences project planning and decision-making.

Assessment:

You'll complete an advanced workbook consisting of six assignments, each 150-300 words.

Module overview:

The master thesis is the culmination of your studies, where you'll undertake an in-depth research project on a topic within artificial intelligence.

Gained skills:

Demonstrate your specialised knowledge and the ability to conduct independent research, analyse data critically, and present your findings effectively.

Real-world application:

Your thesis will contribute original research to the field of AI, potentially influencing industry practices or academic understanding.

Assessment:

You'll complete a 15,000-word master thesis.

Module overview:

Study traditional and advanced approaches to natural language processing (NLP), including key techniques, challenges, and applications in various domains, with an understanding of how cognitive science principles influence language processing.

Gained skills:

You'll master algorithms and methods for solving NLP problems, from text processing to speech pattern recognition.

Real-world application:

Implement NLP techniques in real-world scenarios, such as developing chatbots, improving voice assistants, or enhancing machine translation systems.

Assessment:

You'll deliver a 7-10 minute presentation and write a 1,500-word reflection paper.

Module overview:

Learn the basics of image processing and computer vision, from image acquisition to filtering and texture representation.

Gained skills:

Develop the ability to process and analyse visual data, which is essential for computer vision and image-based systems.

Real-world application:

Use image processing techniques in data science to create systems for object detection, medical imaging, or automated inspection in manufacturing.

Assessment:

You'll take a 90-minute exam consisting of both closed (e.g., multiple choice) and open questions.

Module overview:

This module aims to explore the impact of fintech and AI on the financial industry, including key technologies, applications, and future trends.

Gained skills:

Understand how artificial intelligence and fintech are changing banking, wealth management, and other financial services, along with the associated ethical and regulatory challenges.

Real-world application:

Apply fintech and AI concepts like machine learning to innovate and improve financial services, from blockchain applications to AI-driven customer insights.

Assessment:

You'll complete a 3,000-word written assignment.

Module overview:

Examine the role of AI in transforming healthcare, from drug discovery to personalised care, and consider ethical considerations in AI-driven health technologies.

Gained skills:

Learn to apply AI methods in healthcare to improve outcomes, ensure data protection, and address biases in AI systems.

Real-world application:

Implement AI in healthcare scenarios, such as developing personalised treatment plans or improving the efficiency of clinical trials.

Assessment:

You'll take a 90-minute exam consisting of both closed (e.g., multiple choice) and open questions.

Module overview:

Understand how artificial intelligence changes production systems, from smart factories to autonomous planning and quality control.

Gained skills:

Develop the ability to integrate AI and deep learning into production processes, enhancing efficiency, quality, and innovation in manufacturing.

Real-world application:

Use AI to optimise production lines, improve supply chain management, or implement predictive maintenance strategies.

Assessment:

You'll deliver a 7-10 minute presentation and write a 1,500-word reflection paper.

Module overview:

Learn how AI and machine learning can enhance supply chain management and improve transparency, decision-making, and operational efficiency.

Gained skills:

Apply AI techniques and artificial intelligence to solve supply chain challenges, from risk detection to inventory optimisation.

Real-world application:

Implement AI solutions to improve supply chain resilience and efficiency, addressing fraud detection or demand forecasting challenges.

Assessment:

You'll complete a 3,000-word written assignment.

Module overview:

Explore the use of AI in e-commerce and marketing, covering areas like virtual assistants, visual search, and dynamic pricing.

Gained skills:

Understand how AI can enhance customer experiences, optimise pricing strategies, and address ethical concerns in data usage.

Real-world application:

Use AI techniques to innovate in e-commerce, like developing personalised shopping experiences or improving marketing campaigns.

Assessment:

You'll complete a 3,000-word written assignment.

Module overview:

Explore the complex systems behind self-driving vehicles, including sensor fusion, perception, and decision-making architectures.

Gained skills:

Understand how various components integrate to form autonomous driving systems, focusing on safety and efficiency.

Real-world application:

Apply this knowledge to design and develop self-driving vehicle systems, enhancing reliability and performance.

Assessment:

You'll take a 90-minute exam consisting of both closed (e.g., multiple choice) and open questions.

*You'll be responsible for arranging a suitable internship for your field of study. Once arranged, please complete an application to the internship module via myCampus, to make sure your internship can be recognised and credited towards your course requirements.

Module overview:

Gain practical experience in a professional setting, applying the knowledge and skills you've acquired to solve real-world problems and challenges in AI and computer science.

Gained practical skills:

Get hands-on experience, enhance your professional network, and apply academic learning in a practical context.

Real-world application:

Work on industry projects, contributing to your professional growth and preparing you for a career in computer science.

Assessment:

You'll complete a 3,000-word internship reflection paper (IRP).

An Artificial Intelligence MSc degree is a worthwhile investment as it opens the door to numerous high-demand careers. With the rapid growth of AI in various sectors, graduates with a masters in AI are highly sought after. Our meticulously crafted degree equips you with advanced technical skills in computer science and enhances your problem-solving and critical-thinking abilities, making you a valuable asset in the industry.

The average salary for an Artificial Intelligence MSc graduate in the UK depends on the specific role and industry. According to uk.indeed.com, data scientists earn upwards of £50,000, while machine learning engineers can expect more than £57,500 annually. Specialist positions such as AI engineers can command salaries upwards of £75,000 per year, reflecting the high demand for expertise in this field.

*Salary information from uk.indeed.com and correct as of August 2024.

A Masters in Artificial Intelligence can be challenging due to the complex technical and theoretical concepts involved. Our compulsory modules cover advanced mathematics and programming, providing you with a strong foundation in these areas. And with dedicated support from LIBF’s module lecturers, study coaches and student community, you'll be well-equipped to succeed in your degree.

To thrive in a career in AI, you’ll need a strong foundation in programming languages like Python, as well as expertise in machine learning, data analysis, and deep learning. A solid grasp of mathematics, particularly in statistics and linear algebra, is also key.

Our MSc AI course is designed to equip you with these essential skills and more. Plus, with 24/7 access to resources, support from our AI study assistant, and guidance from dedicated study coaches, you’ll have everything you need to succeed in the industry.

AI specialists are employed across various industries, including technology, finance, healthcare, automotive, retail, electronic engineering and consulting. These sectors leverage AI for data analysis, predictive modelling, automation, and improving customer experiences. The diverse application of AI ensures specialists have a broad range of career opportunities with a postgraduate degree.