Courses Available

Data Spaces

Description:
This course explores data spaces as a next step after data lakes and other data management approaches, highlighting governance as key to AI-ready organizations. It covers governance aspects such as privacy, security, transparency, and monetization. To add a practical dimension, the course introduces various EU initiatives to illustrate data spaces concepts and their implementation.

Production digitalization

Description:
This three-part short course introduces participants to the concepts and practical tools of production digitalization. The course provides hands-on experience with digital simulation, production performance evaluation, flexible systems design, and immersive VR technologies using the Visual Components software. Each module focuses on a key aspect of digital manufacturing and offers structured exercises in a simulated environment.

Practical Applications and Programming of Industrial Robots

Description:
This short course provides participants with a practical and applied overview of industrial robotics in manufacturing. Over three sessions, the course covers the fundamentals of robot-based workplace design, the implementation of robotic operations (e.g., pick-and-place and grinding), and the use of ABB RobotStudio for virtual simulation and programming. Participants will gain hands-on experience in configuring robot work cells, programming robot movements, and optimizing robotic processes in a simulated environment.

Fundamentals of Artificial Intelligence 

Description:
The course covers such topics as Artificial intelligence (AI) definition, historical development, and its prominent role in computer science research. Participants will delve into key concepts and terminology in AI, gaining practical skills in data handling, cleaning, and feature engineering. The course will explore various supervised and unsupervised learning methods, including linear regression, logistic regression, decision trees, ensemble methods, clustering techniques, and dimensionality reduction. Emphasis will be placed on the practical application of tools and libraries for both supervised and unsupervised learning, allowing participants to build, test, and analyze models. Additionally, the course will cover data visualization techniques and explore trends and innovations in AI research, equipping participants with a solid foundation in AI concepts and practical skills for real-world applications.

Artificial Intelligence System Engineering

Description:
The course covers the entire artificial intelligence (AI) project lifecycle, from defining objectives and scoping to deployment and integration, participants will delve into project management methodologies, ethics, and legal considerations in AI development. The course includes hands-on exercises on recognizing and mitigating bias, addressing privacy concerns, and implementing cybersecurity principles. Participants will gain insights into adversarial attacks and defenses, privacy-preserving AI techniques, and documentation strategies. The course concludes with a focus on project closure, exploring success measurement, quality assurance, and emerging trends in IT and AI project management.

Natural Language Processing

Description:
Participants will learn about text-based data sources, retrieval solutions, and data preprocessing techniques, including tokenization and text representation methods. The course delves into sentiment analysis, text classification, part-of-speech tagging, and named entity recognition, with practical exercises in building domain-specific systems. Participants will explore sequence labeling, sequence-to-sequence models, and neural language models for text generation. The course concludes with an overview of question answering, dialog systems, and a review of existing solutions in NLP, along with discussions on new research results and future trends in the field.

Cloud Computing and Big Data

Description:
This comprehensive course delves into the fundamentals of computer networks, cloud computing for AI, and the pivotal role of big data in artificial intelligence (AI). Participants will explore network protocols, architecture, and management alongside cloud computing concepts and services, emphasizing major providers like AWS, Azure, and GCP. The curriculum extends to data management in the cloud, containerization, and orchestration for AI applications. AI model deployment, integration with network services, and strategies for multi-cloud and hybrid cloud deployment are covered. The course also introduces edge computing for AI applications. The big data segment covers data collection, storage, management, processing, and distributed computing. Machine learning and deep learning with big data, as well as real-time analytics and streaming analytics for big data, are explored. The course concludes with a review of existing big data applications, solutions, current research areas, and future trends.

User Experience in Artificial Intelligence Solutions

Business Analytics

Description:
Course topics include data quality, governance, process automation using solutions like RPA, and statistical techniques such as regression analysis and hypothesis testing. The curriculum covers time series analysis, forecasting methods, and explores linear and nonlinear optimization, including integer programming. Decision support through data analytics is addressed, covering areas like customer segmentation, supply chain analytics, and spatial analytics. A/B testing, key metrics for AI projects, and a review of Business Intelligence tools are included, along with an examination of the latest advancements in business analytics.

System Integration

Description:
This course explores the definition and significance of system integration, addressing key challenges and trends in the field. Participants will delve into integration architecture and components, covering relational databases and their optimization, as well as NoSQL databases and data warehousing. Data integration concepts and strategies, including Extract, Transform, Load (ETL) processes, will be examined, along with middleware technologies, message-oriented middleware (MOM), and web services with a focus on RESTful API design and integration. The course also encompasses authentication and authorization requirements, Single Sign-On (SSO) solutions, and cloud computing integration, including serverless and microservices architectures. The IoT ecosystem and devices, mobile app development, cross-platform integration, and the evaluation of integration success will be explored. Additionally, participants will review existing integration tools and platforms, concluding with an exploration of current research areas and the future of system integration and architectures.