This course offers a broad introduction to Artificial Intelligence (AI), its evolution, current trends and future directions. Students learn core concepts including algorithms, machine learning, deep learning, large language models (LLMs), generative AI, prompt engineering and natural language processing (NLP). The course develops logical-algorithmic thinking, breaking complex problems into sub-problems and designing algorithms with flowcharts and pseudocode. Hands-on practice includes AI-driven data analysis and text and sentiment analysis. Students also examine the business value, risks and ethical challenges of AI, such as bias, hallucinations and accountability, in organizations.

This course explores the role of information systems in organizations and their contribution to the modern business environment. Students follow the information system life cycle, from initiation and user requirements through design and implementation, and learn why databases are critical infrastructure. The course focuses on designing relational databases, including entity-relationship diagrams (ERD), normalization and schema design. Hands-on practice in MySQL Workbench builds basic and advanced SQL skills (DDL, DML, inner joins, subqueries). Topics also include decision support systems, transactions, log files, dashboards, cloud adoption, Agile, and information security.

Designed for managers, this course equips students to lead AI-driven innovation and digital transformation in their organizations. It explores how Artificial Intelligence (AI) reshapes business processes, decision-making, forecasting and strategic planning, and how it changes the workforce, managerial roles and required skills. Students analyze AI adoption drivers and barriers using models such as TAM and UTAUT, and examine use cases in supply chain, industrial and healthcare management. Hands-on work applies generative AI and prompt engineering to real managerial problems. The course addresses ethics, regulation and algorithmic bias, and concludes with building an AI strategy roadmap.

This course explores the role of information systems in organizations, from the system life cycle to databases as critical infrastructure. Students design relational databases using entity-relationship diagrams (ERD), normalization and schema design. The course introduces relational algebra as the mathematical foundation of query languages, including selection, projection, union, difference, Cartesian product and natural join, mapped to SQL. Hands-on practice in MySQL Workbench builds basic and advanced SQL skills, with complex exercises on all four JOIN types: INNER, LEFT, RIGHT and FULL. Special emphasis is placed on seamless integration of enterprise systems with AI applications.

This course introduces the core principles of Artificial Intelligence (AI) and the algorithms that enable systems to represent problems, search for solutions, make decisions, learn from data and draw conclusions. Topics include intelligent agents, problem formulation and state spaces, uninformed search (BFS, DFS, uniform-cost search), heuristic search and A*, and adversarial search (Minimax, Alpha-Beta). The machine learning unit covers supervised learning (classification, regression), unsupervised learning and model training. Logic, knowledge representation and inference complete the core, applied to management problems. The course lays the foundation for advanced ML, agents and LLMs.