KDDC 2EN00 - AI Economics with Business of Data and Machine Learning

This module offers students an understanding of data requirements and machine learning modelling capabilities, which can be applied when developing ideas for AI systems in today’s organisation that will address key challenges or opportunities and, ultimately, create value and drive growth under a state of technological disruption. We tend to apply the simple laws of demand and supply to data, computation, and AI-trained human resources, and then venture to understand, in theory and throw digital transformation PACT, which assets and skills AI complements and which it substitutes.

During our journey, the module will shift master students’ focus to developing specific strategic applications of AI to meaningfully solve real business problems. Students will hence be exposed to a wide range of real-world case studies demonstrate Business of Data and Machine Learning. Moreover, they will begin to develop their own concepts and projects for integrated AI systems that could be used in their running thesis or future targeted industry. Application cases from many industries will be presented and covered so that students can see the breadth of potential applications (e.g., energy, space, digital advertising, customer management, new product development, and sustainability/environmental management). Ultimatly, students will also learn about the importance of considering the project organization and human (customer/user and employee/worker) aspects of AI systems and why, despite being based on data and technology, AI systems must be developed from a “humans-first” and organization perspective to drive growth

At the end of this module, students/attendees should be able to:

Understand AI economics and systems from a business data and machine learning perspective

Have a practical business understanding of various commonly used ML/LLM models and their strengths and weaknesses

Reflect on the strategic aspects of the uses of ML in applications of AI in various industries and digital platforms

Critically reflecton the implications ML and AI has for corporate strategy

Demonstrate a broad understanding of the profitable roles of humans in the Big Data ecosystem.

Student will join our class sessions well prepared and ready to contribute to our discussion.Your thorough preparation of all cases is central to the effectiveness of the classes. The more you prepare, the better these will be.

Student will engage with this module's content through assigned background readings, interactive presentations/discussions led by faculty. As always, sessions will be highly interactive, with the session leader/faculty member encouraging you to ask questions, engage in discussions, debate contentious/ethical issues, and share your own experiences.

Remote, open-book exam.

The assessment is designed to test your understanding of the major ideas underpinning the lectures of the module, and your ability to apply them. Questions are designed to test your grasp of the big picture and of the most important concepts,rather than details of algorithms. No coding is required !

Number and type of questions will be similar to the traditional exam format:

There will be 2 parts to the paper.

Part A is worth 40%, and Part B 60%

Answer all questions

On receipt of the questions, student will have one week to complete and submit your response.

Once the questions are released you should not discuss the exam with other candidates.

The word count will be maximum 1500 words total across all questions. The word count includes the main body of text, including in-text citations, direct quotations, tables, figures, and diagrams, and excluding appendices, footnotes, reference list and bibliography.