Skip to main content

ISLA Santarém 27437

Artificial Intelligence Applied to Marketing

Marketing
  • ApresentaçãoPresentation
       
  • ProgramaProgramme
    1 Introduction to Artificial Intelligence (AI) in Marketing. 2. Fundamentals of AI and Machine Learning. 3. Applications of AI in Digital Marketing Strategies. 4. Predictive Analysis and Forecasting Market Trends. 5. Personalization and Customer Segmentation with AI. 6. Pattern Recognition and Sentiment Analysis on customers. 7. Optimizing Advertising Campaigns with Intelligent Algorithms. 8. Optimizing Customer Service with Chatbots and Virtual Assistance. 9. Case Studies and Practical Examples of AI Applications in Marketing. 10. Emerging Perspectives and Trends in AI in Marketing.
  • ObjectivosObjectives
    O1. Distinguish the differences between Artificial Intelligence (AI) and Machine Learning. O2. Analyse the implications of AI technologies in the context of marketing-related activities, namely in the development of predictive analyses and forecasts of market trends, customer personalization and segmentation, campaign optimization, etc. O3. Apply AI technologies in the context of marketing strategies. O4. Discuss the impact of artificial intelligence on the workforce, marketing processes and consumption in the future.
  • BibliografiaBibliography
    Aggarwal, C. C. (2021). Artificial Intelligence A Textbook. Springer. Gentsch, P. (2019). AI in Marketing, Sales and Service. How Marketers without a Data Science Degree can use AI, Big Data and Bots. Palgrave Macmillan Cham Kaput, M. (2022). Marketing Artificial Intelligence. BenBella Books Russell, S., & Norvig, P. (2021). Artificial intelligence: a modern approach. Pearson. Vvaa (2024). Inteligencia Artificial Para El Marketing. ESIC Editorial.  
  • MetodologiaMethodology
    The teaching methodology combines synchronous online activities, in-person sessions, and independent study. The online sessions focus on presenting and exploring the content through expository, demonstrative, and interrogative methods, including formative assessment activities conducted on the Moodle platform. In-person classes utilize simulated exercises, incorporating AI tools, the analysis and discussion of case studies, and practical applications using Python (Anaconda, Scikit-learn, and Pytholog), including integration into a cloud computing environment (AWS). Independent study is based on guided research, conducted in accordance with scientific research principles, to explore the topics covered in greater depth. The instructor provides support through tutorial guidance and feedback, either in person or via Moodle.
  • LínguaLanguage
    Português
  • TipoType
    Semestral
  • ECTS
    5
  • NaturezaNature
    Mandatory
  • EstágioInternship
    Não
  • AvaliaçãoEvaluation