MSc APPLIED ARTIFICIAL INTELLIGENCE

Overview

Mode	Duration	Start date	Application code	Application method
ModeFull-time	Duration2 years	Start dateJanuary	Application code5795	Application method Direct to LSBU
ModeFull-time	Duration1 year	Start dateSeptember	Application code5794	Application method Direct to LSBU
ModePart-time	Duration2 years	Start dateSeptember	Application code5796	Application method Direct to LSBU

Home/EU applicants

Mode	Duration	Start date	Application code	Application method
Mode Full-time	Duration 2 years	Start date January	Application code 5795	Application method Direct to LSBU
Mode Full-time	Duration 1 year	Start date September	Application code 5794	Application method Direct to LSBU
Mode Part-time	Duration 2 years	Start date September	Application code 5796	Application method Direct to LSBU

Postgraduate students and research students should apply through our dedicated application system. Full details of how to do this are supplied on our How to apply section for postgraduate students and our How to apply section for research students.

See our admissions policy (PDF File 1,043 KB) and complaints policy (PDF File 516 KB).

Accommodation

Students should apply for accommodation at London South Bank University (LSBU) as soon as possible, once we have made an offer of a place on one of our academic courses. Read more about applying for accommodation at LSBU.

Finance

It's a good idea to think about how you'll pay university tuition and maintenance costs while you're still applying for a place to study. Remember – you don't need to wait for a confirmed place on a course to start applying for student finance. Read how to pay your fees as a postgraduate student.

Postgraduate Application Service

Book a session with one of our specialist Postgraduate Advisors. Over a one on one Advice Session they'll advise you on postgraduate degrees at LSBU that match your interests and experience.

Prepare to start

We help our students prepare for university even before the semester starts. To find out when you should apply for your LSBU accommodation or student finance read the How to apply tab for this course.

Enrolment

Before you start your course we’ll send you information on what you’ll need to do before you arrive and during your first few days on campus. You can read about the process on our Enrolment pages.

• Foundations of calculus, statistics, and optimization
  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.
• Python programming for AI and Visualization
  This module provides an intensive introduction to programming, especially for students without a computer science background.
  The module will cover the basics of program development in Python, with specific attention to its use in visualizing and exploring data, and in translating mathematical problems and models into a computational solution.
• Machine Learning
  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.
• Deep Learning
  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.
• Industrial Cyber-Physical Systems
  Industrial Cyber-Physical Systems (ICPS) are real-world networked industrial production systems having a cyber-representation through the digitalisation of data and information across the enterprise and value chain. This module offers a comprehensive overview of ICPS and outlines the challenges related to the fourth industrial revolution commonly referred to as Industry 4.0. The module consists of two parts. The first part will introduce the theoretical framework and supporting technologies of the fourth industrial revolution (e.g., industrial internet-of-things, digital twins, etc.). The second part focuses on system design and implementation aspects of ICPS along with multiple tutoring sessions.
• Natural Language Processing
  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.
• Computer Vision and Pattern Recognitio
  The pervasive nature of visual tasks in everyday life, and the amount of imaging information accrued by mobile, wearable and classical devices, calls more and more for sophisticated tools able to relieve the burden of image scanning and screening from human users. Additionally, many task can benefit from image-guided augmented reality scenarios (e.g. surgery), or can be automated through visual-guidance (robotics).  
  The module covers the theoretical and practical aspects of image processing and analysis, image understanding and computer vision:
  after introducing the basics principles and concepts of image formation and image representation, it will present the fundamentals of image processing. It then will dive in modern approaches to the processing of images and videos: object detection, object recognition, scene understanding  using machine learning and artificial intelligence
• Future Internet Technologies
  Future Internet technologies comprise set of enablers to deal with the limitation of the existing Internet. This includes but not limited All-IP Networking Architectures, the evolution towards 4G+/5G networking architectures, open-based networking technologies, Cloud Computing challenges and IoT technologies and its interworking with 4G+/5G networks. The module will provide both theoretical knowledge and practical exposure to the students.
• Data Management
  Data Management and Data Quality are increasingly at the heart of every commercial and research enterprise, as the value of data increases along with the need for adaptive security. This module will give you a critical and evaluative knowledge of the theory, practice and research of software engineering techniques for Data Management amid today’s complex and changing business/commercial/research environments.
• Data Mining and Analysis
  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.
• Robotics
  The module introduces students to the basic elements and principles of robotics. Essential geometric concepts of rigid-body displacements will be introduced and applied to the kinematic analysis of different machines.   Differential kinematics will be developed and applied to study the statics and dynamics of robots.  Some problems concerned with the control of these machines will be considered.
  Laboratory exercises are designed to consolidate understanding of robot kinematics and programming as well as giving an appreciation of practical considerations.
• Ethics in AI and Seminars
  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.
• Research Methods
  The module provides students with the ability to discuss, evaluate and use a variety of research methods and techniques for their chosen subject area of Computer Science and Informatics and to develop a professional and ethical approach to carrying out research-based projects. It also equips students with entrepreneurial and innovation skills increasingly sought by industry and employers.
  
  NOTE: You will be required to pass the module before progressing to the Dissertation stage of your studies. In order to pass this module, students must obtain a weighted average grade of at least 50%.
• Dissertation
  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.

All modules are assessed by a mix of coursework and examinations.