AI and Machine Learning for Marketers

During your course, you will study a total of 6 core modules. These will be taught on a week-by-week basis:

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

In addition, 2 free untutored marketing modules have been devised in collaboration with IDM to provide marketers with an in-depth understanding of how to implement machine learning to enhance processes and boost efficiency.

Both ‘Machine learning’ and ‘Application of machine learning in marketing’ can be studied in conjunction with your other modules, to further supplement those lessons.

Alternatively, you can study both the untutored marketing modules at the end of week 6, if preferred.