AI and Machine Learning Essentials

Professionals, working with data but not directly involved in data science roles and who want to leverage AI/ML possibilities without needing deep technical skills, such as:

• Managers, business analysts, product managers, looking to integrate AI/ML into decision-making and strategy
• Consultants, advisors, and lifelong learners seeking practical AI/ML applications across industries

In today’s rapidly evolving landscape, AI and Machine Learning (ML) are reshaping industries, creating new possibilities for professionals
in every field. Even if your role is not directly in AI or data science, understanding how these technologies work can equip you to enhance efficiency in your day-to-day business activities and help you stay at the forefront of the latest developments in your area of expertise.

How can AI tools help you increase productivity, enhance decision making or streamline processes? Through real-world case studies and interactive exercises, get inspired to uncover new AI-driven opportunities.

• Develop an understanding of how AI and machine learning models are built, including tools like ChatGPT
• Identify how AI applies to your function, and recognise both opportunities and risks
• Gain hands-on experience with AI tools to improve productivity and efficiency in your daily tasks
• Explore the capabilities and limitations of Large Language Models (LLMs) like ChatGPT and understand their practical uses
• Develop the ability to effectively communicate and collaborate with data science professionals in various professional contexts

A certificate of attendance will be delivered at the end of the course.

DAY 1 – Morning
Introduction to Machine Learning
Frontal lesson with conceptual exercises

• Building blocks of AI (domain expertise, data, algorithms, infrastructure)
• Different types of learning (supervised, self-supervised, unsupervised, multimodal)
• Examples of real-world applications of AI

DAY 1 – Afternoon
Case studies in Machine Learning
Hands-on exercises, discussion and initial brainstorming

• Data science pipeline essentials
• Hands-on case studies with interactive exercises
• Brainstorming on how to frame a machine learning problem

DAY 2 – Morning
Large Language Models (LLMs) and Generative AI
Frontal lesson and hands on exercises

• Introduction to Generative AI
• Applications and limitations of Generative AI models
• Considerations from a business perspective (model size, fine-tuning, RAG systems, etc)

DAY 2 – Afternoon
Opportunities in AI

• Operational considerations for AI projects
• AI project ideation (working in groups)
• Discussion of selected projects (problems encountered and lessons learned)
• Conclusion: Insights and key takeaways

Programme Director

• Prof. Negar Kiyavash, Full Professor, Chair of Business Analytics, EPFL

Instructors

• Christian Lübbe, Machine Learning Course Instructor, EPFL
• Amir Khalilzadeh, Machine Learning Course Instructor, EPFL

Dates and schedule

Next course dates:

• May 23-24, 2025: registration closed
• November 25-26, 2025: registration open
  • Tuesday, 25 November, 2025
    9:00 am to 5:00 pm
  • Wednesday, 26 November, 2025
    9:00 am to 5:00 pm

Course venue

EPFL, Lausanne, Switzerland

Material

Participants should bring their own laptop (for hands-on experience).

Course fee

CHF 1’500.–*

*10% special discount for contributing members of EPFL Alumni , EPFL partners and for Alumni of the EDS, FDS, TCC, ADSCV and ADSML programs.

Registration deadline

August 31, 2025