Teacher: Prof. Yash Raj Shrestha
Teaching Assistant: Yuanjun Feng & Henri Jamet
Bachelor (3rd year) 2h/week – 3 ECTS
Spring Semester Thursdays 16:15 to 18:00
Moodle Link here
Course Catalogue link here
Objectives
By synthesizing robust patterns from large data sets, Artificial Intelligence (AI)—and in particular machine learning algorithms—enable the creation of new information and predictions from data. The promise of fast, accurate, and low-cost decisions with quality approaching human-like intelligence, has been an important driver of the rapid developments in AI. Indeed, AI has found application in various processes within business organisations—including marketing (e.g., customer journey prediction), human resource management (e.g., hiring decisions), finance (e.g., credit risk predictions), etc. While the rapid adoption of AI attests to the many measurable benefits of AI’s learning and prediction power, its application in business needs to be based on an adequate understanding of its strengths and weaknesses.
The overall objective of this course is to develop students’ understanding of the applications of AI within business functions such as business model development, marketing, supply chain, finance, and human resources. We will look at this intersection in various industries including BigTech, Automobiles, Media and Software. We will also conduct a critical examination of various challenges facing AI application in business and potential solutions in resolving them. At the end of the course, students should be able to:
1. Identify opportunities within business processes for applying AI applications and developing such applications.
2. Identify problems in AI adoption, propose a feasible solution in resolving them and structure and explain its reasoning.
3. Critically examine the benefits and risks of AI in business
This course comprises of three components:
1. In-class theoretical lectures: In class theory lectures are aimed to build a fundamental knowledge of business environment and use cases of AI within businesses
2. Group work on business case and hands-on assignment: Business case assignment in the form of consulting case is designed to help students critically examine AI applications in a real world setting.
3. Guest lecturers: We will invite guests from diverse industries who are already leading AI projects in their businesses to share their experiences and outlook.
The evaluation consists of two parts:
- Consulting Case presentations with group assignments (50%),
- 1 hour exam ENEP in moodle (50%) covering the entire course.
Question and answer language for both “consulting case presentations” and “written exam” is English.Resit exam will consist of 1 hour Written Exam covering the entire course.
Sample Exam Questions is available here (to be updated as we move in the semester)
[See Moodle for more information]