AI product management

Summary

The course focuses on the development of real-word AI/ML products. It is intended for students who have acquired a theoretical background in AI/ML and are interested in applying that toward developing AI/ML-oriented products.

Content

Keywords

Artificial Intelligence (AI), AI product managers, Innovation

Learning Prerequisites

Required courses

CS-233 Introduction to machine learning or CS-433 Machinie learning or equivalent course on the basics of machine learning and deep learning

Important concepts to start the course

• Python programming
• Bascis of deep learning and machine learning
• Basics of probability and statistics

Learning Outcomes

By the end of the course, the student must be able to:

• and understand opportunities for an AI product or using AI within an existing product
• the development of AI features
• Launch AI products successfully

Transversal skills

• Demonstrate the capacity for critical thinking
• Evaluate one's own performance in the team, receive and respond appropriately to feedback.
• Communicate effectively, being understood, including across different languages and cultures.
• Set objectives and design an action plan to reach those objectives.
• Chair a meeting to achieve a particular agenda, maximising participation.
• Resolve conflicts in ways that are productive for the task and the people concerned.
• Make an oral presentation.
• Take account of the social and human dimensions of the engineering profession.

Teaching methods

• Formal lectures
• Group activities
• Class discussions
• Simulation games
• Hands-on exercises
• Project-based learning
• Real-world case studies
• Guest lectures by leading academic and industry figuers

Expected student activities

• Individual : Case evaluations, self-study, class discussions
• In-group : In-class exercises, projects, simulations games
• Presentation : Weekly presentations of assignments in coaching sessions

Assessment methods

Continuous evaluation of case reports, proejcts, individual and group presentations, class discussions, during the semester. More precisely :

25% Weekly in-class work and engagement

45% Class assignments, presentations, projects, and case reports

30% Final (final report and presentation and understanding of the case)

Supervision

Resources

Bibliography

• Cagan, M. (2017). How to Create Tech Products Customers Love. Wiley
• Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A flaw in human judgment. Little, Brown.
• Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: strategy and leadership when algorithms and networks run the world. Harvard Business Press.

Ressources en bibliothèque

• How to Create Tech Products Customers Love / Cagan
• Competing in the age of AI / Iansiti
• Noise / Kahneman

Moodle Link

• https://go.epfl.ch/CS-500