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AI and Machine Learning for Business

This course will introduce you to the core capabilities of Artificial Intelligence (AI) and empower you to contribute to this exciting and transformational new era in global technological development.

Developed by the pioneering Data Science team at the University of Southampton – ranked among the top 100 universities globally – this course will equip you with the specialist knowledge and skills you need to understand and effectively apply AI technologies within your organisation.

Taught by industry-leading experts in AI and data science using a practical approach to learning, this 6-week flexible online course will see you exploring a range of AI capabilities in the context of real-life business case studies – teaching you how you can effectively harness the power of AI to transform your business.

What you’ll learn

The course runs over 6 weeks and is broken down into manageable weekly topics:

Week 1: Introduction to AI

  • What AI is and its main classes of applications and capabilities
  • The differences between different types of AI technologies
  • Core technologies associated with AI
  • The relationship between AI and other technology trends such as big data, cloud computing, and the Internet of Things (IoT)
  • The role of data in AI
  • The challenges in applying AI within organisations
  • The limitations of AI

Week 2: Case study – Learning to know your customers

  • The difference between supervised and unsupervised machine-learning algorithms
  • Fundamental classes of machine-learning, including regression, classification and clustering
  • Types of business problems machine-learning can solve and machine-learning tasks that can be used to solve them
  • Activities and technologies used to build a Natural Language Processing (NLP) pipeline
  • Statistical processing and work distributions
  • Applying regression, classification, and clustering to extract information and recommend items to purchase
  • Analysis, assessment and interpretation of the results of machine-learning models

Week 3: Case study – Enhancing the customer experience

  • The Turing test and how it can be used to improve AI systems
  • Important methods and technologies in natural language generation
  • Deep-learning approaches to NLP and what they’re used for
  • Important methods and tools in natural language understanding and speech recognition
  • Designing conversational agents (i.e. chatbots)

Week 4: Case study – Search and recommendation

  • Clustering algorithms
  • Topic modelling
  • Knowledge bases: How are they built? What purpose do they serve?
  • Using a knowledge base for Named Entity Recognition (NER)
  • Introduction to the semantic web
  • Using the knowledge base to extract relevant information (i.e. SPARQL and Google Knowledge Graph)

Week 5: Case study – Computer vision

  • Traditional approaches to image-processing and computer vision
  • Image classification and clustering
  • Feature extraction
  • Convolutional neural networks (CNNs)
  • Combining CNNs with conversational agents to generate textual descriptions
  • Systems for automatic surveillance

Week 6: Future directions for AI

  • Current limitations
  • Technological advances
  • Societal and cultural shifts
  • Ethical, moral and legal issues

After successfully completing the course, you’ll be able to:

  • Understand what AI technology is, its capabilities and limitations, and the potential benefits it can bring to your business
  • Identify AI’s main capabilities and the relevant technologies needed to deliver them
  • Explain the different components needed to deliver complex AI systems
  • Discuss the ethical, moral and legal implications of AI in various areas of today’s society
  • Identify different types and applications of data in delivering effective AI solutions
  • Identify various software that can be used to process, analyse, and draw meaning from natural language as well as from images and numerical data – enabling deeper insights

Professor

Les Carr

Les is Professor of Web Science at the University of Southampton’s School of Electronics and Computer Science. He’s also a Director of the Web Science Institute and former Director of the Web Science Centre for Doctoral Training. His research on Open Access and Open Data led to the establishment of Eprints – offering Open Access publication and data services, training and support to the research industry.

Professor

Elena Simperl

Elena is a Professor of Computer Science within the Web and Internet Science research group – part of the University of Southampton’s School of Electronics and Computer Science. Her primary research domain concerns the intersection between knowledge technologies and crowd computing. Elena is interested in socially and economically-motivated aspects of creating and using semantically-enabled content on the Web, and in paradigms, methods and techniques to facilitate large-scale collaboration and incentivise participation.

Doctor

Rob Blair

Rob is Senior Teaching Fellow at the University of Southampton’s School of Electronics and Computer Science. He holds an MSc Information Systems from the University of East Anglia and an MSc Web Science from the University of Southampton. Qualified to teach Physics, Mathematics and Computer Science, Rob is a highly-experienced classroom teacher and online tutor specialising in data science.


David Millard

Dave is Associate Professor of Computer and Web Science at the University of Southampton, David is a founding member of the Web and Internet Science research group within the School of Electronics and Computer Science (ECS). He represents ECS on the steering group for the Web Science Centre for Doctoral Training. David is currently Vice-Chair for ACM SIGWEB.


Gary Wills

Gary is an Associate Professor in Computer Science at the University of Southampton. He graduated from the University of Southampton with an Honours degree in Electromechanical Engineering, followed by a PhD in Industrial Hypermedia Systems. Gary is a Chartered Engineer, a member of the Institute of Engineering Technology, and a Principal Fellow of the Higher Educational Academy. He is also a visiting Associate Professor at University of Cape Town and a research professor at RLabs.

Ready to boost your data literacy?

Take our flexible online course and learn how to leverage data to achieve your business goals.

Apply now