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273-0003-00L 5 Credits WBZ , NDS D-INFK

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Building ML/AI Applications

Lecturers & Examiners: Dr. Carlos Cotrini Jimenez

VVZ CR n/a

Last Updated: 2026-06-03 00:07:32

Abstract

This course is for participants with a management background seeking a solid understanding of AI and its practical application. It covers machine learning, natural language processing, computer vision, large language models, and agentic AI, combining conceptual foundations with hands-on projects that equip participants to drive AI-led innovation within their organisations.

Objective

- Understand how modern AI applications are designed, trained, and validated. - Identify and implement the appropriate technical solution for AI problems arising in computer vision and natural language processing. - Understand contemporary methods for agentic AI and their application to business processes. - Critically assess the capabilities and limitations of current AI technologies in order to support informed strategic and operational decisions.

Content

The course is delivered across four intensive weekends. Each weekend concludes with a hands-on project that allows participants to practise and consolidate the concepts covered: - Weekend 1: Foundations of AI. Participants explore the principles underpinning modern AI technologies, including how models are designed, trained, and validated. - Weekend 2: Computer Vision. Building on the foundations, this weekend examines recent innovations in computer vision, including U-Nets and diffusion models. - Weekend 3: Large Language Models and Reasoning. Participants study the architecture and capabilities of large language models, with particular focus on reasoning-oriented systems and their role in the broader trajectory toward artificial general intelligence. - Weekend 4: Conditional Diffusion and Image Editing. The final weekend is dedicated to image editing using conditional diffusion models, integrating the techniques acquired throughout the programme.

General Information

Language: English
Levels: WBZ , NDS
Frequency: Semesterly recurring

Examination

Type: ungraded semester performance

This course is graded pass/fail. The grade is determined by a set of projects where participants develop in teams ML applications on realistic data.

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type	Title	Time & Place	Hours
lecture	Building ML/AI Applications Block course	No time listed	36 h semesterly