AI in Practice: Implementation and Strategy
This course is part of AI in Practice.
This comprehensive course demystifies AI implementation in organizations, focusing on practical applications rather than complex algorithms. Students learn from real-world case studies across healthcare, finance, retail, and telecommunications sectors, featuring organizations like ING, Radboud UMC, and Ahold Delhaize. The course covers reinforcement learning applications, diagnostic image analysis, AI strategy development, and societal impacts. Designed for managers, analysts, and practitioners, it provides hands-on guidance for integrating AI solutions into existing business processes. Participants will gain practical knowledge about implementation challenges, lifecycle management, and organizational requirements for successful AI adoption. By course end, students will be equipped to develop and execute AI implementation plans tailored to their organizations.
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What you'll learn
Describe benefits and challenges of implementing AI in organizations through real-world examples
Identify essential conditions and requirements for successful AI implementation in industry and academia
Understand practical implementation aspects of AI and their relevance to organizational success
Develop comprehensive plans for applying AI solutions in your organization
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This course covers five main topics in AI implementation: Reinforcement Learning in FinTech with real-world applications from ING, Diagnostic Image Analysis focusing on COVID-19 detection, AI Strategy and Implementation aspects across various sectors, Agent Architecture in law enforcement, and AI for Society through civic applications. The curriculum combines theoretical knowledge with practical case studies from leading organizations, emphasizing hands-on implementation strategies and real-world challenges.
Reinforcement Learning for Real Life
Module 1
Diagnostic Image Analysis for COVID-19
Module 2
Thematic Track on AI Strategy and Implementation
Module 3
Agent Architecture of the Intake
Module 4
AI for Society
Module 5
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: AI in Practice
Instructors
8 Courses
Software Engineering Pioneer and AI Innovation Leader
Arie van Deursen serves as Professor of Software Engineering at TU Delft, where he heads both the Software Engineering Research Group and the Department of Software Technology while directing the AI for Fintech Research lab, a five-year collaboration between ING and TU Delft. After earning his MSc in Computer Science from Vrije Universiteit Amsterdam and PhD from the University of Amsterdam, he conducted research at CWI before joining TU Delft as a professor in software engineering. His research spans software engineering, software testing, trustworthy AI, and human aspects of software engineering, with particular focus on empirical research conducted in close collaboration with industry. As scientific director of AI-for-Fintech Research, he leads initiatives involving 10 PhD students and numerous faculty members, while serving on the advisory board of ICAI and co-leading its Long Term Program on Trustworthy AI. His entrepreneurial spirit is evidenced by co-founding two successful companies: The Software Improvement Group (2000) and PerfectXL (2010). Recently elected as a Fellow of the Netherlands Academy of Engineering in 2023, he has supervised over 35 PhD students and 75 MSc students while serving as program co-chair for prestigious conferences like ICSE 2021 and ESEC/FSE 2017. Through his roles as member of the Advisory Council of IT Assessment for the Dutch government and the advisory council of ING Bank Netherlands, he continues to bridge academic innovation with practical applications in software engineering and artificial intelligence.
3 Courses
A Pioneer in Medical Image Analysis and Artificial Intelligence
Bram van Ginneken, born in Nuenen in 1970, serves as Professor of Medical Image Analysis at Radboud University Medical Center, where he chairs the Diagnostic Image Analysis Group and leads Europe's largest medical image analysis research team. His academic journey began with physics studies at Eindhoven University of Technology and Utrecht University, culminating in a PhD from the Image Sciences Institute in 2001 focusing on Computer-Aided Diagnosis in Chest Radiography. His groundbreaking PhD research led to the development of CAD4TB, now the most widely used autonomous AI solution for medical image interpretation, installed in over 75 countries worldwide. Beyond academia, he co-founded Thirona in 2014, a company specializing in CT lung image analysis software, and maintains a position at the Fraunhofer Institute for Digital Medicine MEVIS in Bremen, Germany. His scientific impact is reflected in over 300 publications in international journals, with his work garnering more than 79,000 citations. As a member of the Fleischner Society and the Editorial Board of Medical Image Analysis, he has significantly influenced the field by pioneering the concept of challenges in medical image analysis and creating grand-challenge.org. His leadership has fostered interdisciplinary collaboration between biology and Artificial Intelligence, particularly in developing novel algorithms for clinical applications, making him a key figure in advancing healthcare through technological innovation.
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