About this course
One way or another, AI will determine the future of technology. While it has vocal supporters and opponents, there is no doubt that its impact will be transformational. While the field is changing rapidly, there is a lot to learn, but also many ways to shape it and contribute. This course teaches you what you need to know to be part of this journey.
AI has delivered some of the most amazing technological advances of the last decade, outperforming human abilities in domains as diverse as image recognition, natural language understanding, pattern detection, prediction and autonomous devices. It has shown that it can transform entire industries over the course of a few years, and change the way we think about our lives, jobs, businesses, government, and society.
The course is organized according to a framework of core AI capabilities. It follows a problem-based learning approach, where each AI capability is discussed in the context of a business case study. It covers a mix of learning materials, including short video tutorials, guided walkthroughs, presentations, online exercises and further reading and has been designed to enable professionals in the public and private sectors to gain the knowledge and skills that allow them to understand how to use AI in their organization.
Aims and learning outcomes
At the end of this course, you will have developed the skills and knowledge to be able to identify potential applications for AI in your business. You will be able to:
- Explain what AI is, the role it can play and the potential benefits it can bring your organization
- Identify the primary capabilities of AI and the core associated technologies needed to deliver them
- Outline the different components required to deliver complex AI systems such as autonomous cars or intelligent assistants
- Discuss the ethical implications of AI in different areas of the economy, government, and society
- Identify different types, characteristics, and uses of data in AI solutions
- Describe fundamental classes of information extraction, clustering, prediction, as well as search and planning techniques
- Identify software which can be used to process, analyze, and extract meaning from natural language, images, and numerical data to develop insights and understanding
Assignment 1 - Identify and articulate an AI opportunity for your business
Based upon topics introduced in the first week of the course you are asked to identify a ‘simple’ issue within your organization which you believe could be improved or resolved through development and application of an AI solution.
Assignment 2 - Plan and quantify how to make this opportunity happen
Considering each of the case studies introduced in weeks 2, 3 and 4 you are asked to produce a report for a non-specialist audience. You will compare and contrast the types of AI solution developed in each study, the rationale for selection of the AI solution, the benefits to the organization and the expected ‘value’ or return on investment of the solution.
Assignment 3 - Apply what you have learned in the course to hone your proposal
Utilising knowledge gained through a discussion of each case study you will revisit the simple issue identified in the first assignment and propose a revised solution. You are tasked with producing a report describing an AI-based solution for this issue, explaining why and how your revised solution differs from the original solution whilst also addressing any legal, moral or ethical issues identified.
Week 1 - Introduction to AI
- Understand what AI is and its main classes of applications and capabilities
- Understand the difference between different types of AI and have an overview of the state of the art in each of these areas
- Be familiar with the core technologies associated with AI and the main players in the field
- Understand the relationship between AI and other technology trends such as Big Data, Cloud Computing, or the Internet of Things (IoT)
- Understand the role of data in AI
- Understand the greatest challenges of applying AI in organizations, including data quality, transparency, biases, and privacy
- Understand the limitations of AI
Week 2 - Case study: Learning to know your customers
- Understand the difference between supervised and unsupervised machine learning algorithms
- Understand fundamental classes of machine learning, such as regression, classification, and clustering
- Understand what types of problems machine learning can solve and be able to select machine learning tasks that are useful in an application context
- Understand the main activities and technologies used to build a Natural Language Processing (NLP) pipeline
- Statistical processing and word distributions
- Learn how to generate features from textual data to serve as input to machine learning models
- Apply regression, classification, and clustering to extract information and recommend items to purchase
- Apply supervised classification to perform sentiment analysis
- Analyse, assess and interpret the results of machine learning models
Week 3 - Case study: Enhancing the customer experience
- Understand what the Turing test is about and how it can be used to improve AI systems
- Get familiar with the most important methods and technologies in natural language generation
- Get an overview of deep learning approaches for NLP and what they are used for
- Understand the most important methods and tools in natural language understanding and speech recognition
- Learn how to design conversational agents (i.e. chatbots)
Week 4 - Case study: Search and recommendation
- Clustering algorithms
- Topic modeling
- 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 nets: A biologically-inspired model.
- A picture is worth of 100(0) words: Coupling of Convolutional Neural Networks (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 issues
- Moral issues
- Legal issues
- Duration: 60 hours (across 6 weeks)
- 1st October - 9th November
- 5th November - 14th December
- Course lead: Elena Simperl
- 100% online delivery: Personal and group tutorials, guided self-study via core materials, Q&A and peer discussions
- Prerequisites: Good basic understanding of technology. Previous coding experience is not essential
- Target group: Business practitioners, analysts, consultants, managers, executives, etc.
- Learning materials: Guided walkthroughs, video tutorials, slide decks, online exercises, further reading
- Assessment and feedback: 3 pieces, 33% each. Feedback at the end of the course, consisting of marks and comments for each assignment
Program taught in: