This course aims to provide students with a foundational understanding of artificial intelligence (AI) and data engineering, along with the ethical considerations that arise in these fields. Students will explore key concepts and techniques in AI, including machine learning, data processing, and algorithm development, while also learning about the tools and technologies used in data engineering to manage and analyze large datasets. The course emphasizes the importance of ethical practices in AI and data engineering, addressing issues such as bias, privacy, and the societal impact of AI technologies. By the end of the course, students will be equipped with the technical knowledge and ethical framework needed to responsibly design, implement, and evaluate AI and data engineering solutions.

This course offers a comprehensive introduction to the fundamental principles of artificial intelligence (AI) and data engineering, with a strong emphasis on ethical considerations. Students will learn about key AI concepts such as machine learning, neural networks, and data-driven decision-making, alongside data engineering techniques for processing, storing, and analyzing large datasets. The course also explores the ethical challenges associated with AI and data engineering, including bias, privacy, and the societal impacts of these technologies. Through a combination of theoretical learning and practical exercises, students will gain the skills and ethical awareness necessary to develop and manage AI systems and data engineering projects responsibly.

Face-to-face lecture, online course materials, visual presentations, discussions

Resources: Lecture Notes and Codes Textbooks: 1. Artificial Intelligence: A Modern Approach (4th Edition) by Stuart Russell and Peter Norvig, Pearson (2022). 2. Fundamentals of Data Engineering by Joe Reis and Matt Housley, O'reilly (2022). 3. Designing data-intensive applications: The big ideas behind reliable, scalable, and maintainable systems by M. Kleppmann, O'reilly (2017).