AI Skills: Machine Learning Fundamentals

Tom Viering serves as an Assistant Professor in the Pattern Recognition and Bio-Informatics research group at TU Delft's Faculty of Electrical Engineering, Mathematics & Computer Science, where he coordinates and teaches in the university's AI minor program. After completing his MSc in Computer Science Media Knowledge Engineering at TU Delft, he earned his PhD focusing on explainability, active learning, and learning curves. His research interests span theoretical machine learning topics, including statistical learning theory, active learning, and domain adaptation. As an educator, he has developed several courses from scratch, including 'Introduction to Machine Learning' and 'Capstone Applied AI project,' and co-created MOOCs on Supervised Machine Learning. His innovative teaching approach incorporates interactive Python widgets to enhance student understanding, and he actively works on developing open education materials for machine learning. Beyond teaching, his research contributions include significant work on learning curve patterns, generalization bounds, and the relationship between data quantity and performance in machine learning systems. He has published extensively in prestigious venues, with his work on learning curves, active learning, and AI safety gaining notable recognition in the academic community. As coordinator of the AI minor program, which launched in 2021, he plays a crucial role in making AI education accessible to engineers across various disciplines at TU Delft.

Hongrui Wang serves as an Assistant Professor in Artificial Intelligence and Digital Infrastructures at TU Delft's Department of Engineering Structures, recruited under the TU Delft AI Initiative. His academic journey began at Southwest Jiaotong University in China, where he earned his Bachelor's degree in Electrical Engineering and Automation from the prestigious Mao Yisheng Class. He completed his PhD at TU Delft in 2019, focusing on data-based structural health monitoring of railway catenary infrastructures, followed by a postdoctoral position before assuming his current role. As Associate Editor-in-Chief of IEEE Transactions on Instrumentation and Measurement, he has been recognized as an Outstanding Associate Editor for three consecutive years. His research lies at the intersection of artificial intelligence and engineering structures, developing innovative AI techniques for structural health monitoring, lifecycle asset management, and digital modeling. Wang's work has garnered significant recognition, including the PhD thesis award from the European Rail Research Advisory Council and multiple highly-cited publications in engineering. His current research focuses on physics-informed neural networks, machine learning applications in structural engineering, and the development of smart railway infrastructures, while actively contributing to education in data science, artificial intelligence, and emerging technologies in transportation systems.

Amira Elnouty serves as a lecturer in the Computer Science & Engineering Teaching Team at TU Delft, where she specializes in multimedia courses including Signal Processing and Image Processing. Her academic background includes an MSc in Informatics from Nice University and a BSc in Computer Engineering from Cairo University. Originally from Egypt, she joined TU Delft in July 2018 and has since become an integral part of the university's dedicated teaching team. Her expertise spans computer vision and machine learning, with a particular focus on incorporating gamification and critical thinking into computer science education. She is passionate about behavioral science and psychology, striving to integrate these fields with computer sciences. As part of the multimedia variant bachelor track, she emphasizes interactive teaching methods and student engagement. Her teaching philosophy centers on creating dynamic classroom environments and fostering critical thinking skills among students. She actively contributes to the AI Skills program at TU Delft, particularly in areas of unsupervised learning and deep learning, while maintaining a strong commitment to innovative educational practices.

Luca Laurenti serves as a tenure-track Assistant Professor at the Delft Center for Systems and Control at TU Delft and co-directs the HERALD Delft AI Lab. His academic journey includes a PhD from the University of Oxford's Department of Computer Science, where he was a member of Trinity College. With expertise spanning stochastic systems, control theory, formal methods, and artificial intelligence, his research focuses on developing robust data-driven systems that can reliably interact with dynamic and uncertain environments. His work has garnered significant attention in the academic community, particularly in areas such as Bayesian neural networks, uncertainty quantification in autonomous systems, and safety certification for stochastic systems. He has made notable contributions to the field of autonomous driving control and robustness certification of AI systems, with his publications appearing in prestigious venues such as IEEE International Conference on Robotics and Automation and Neural Information Processing Systems. As an educator and researcher, Laurenti combines theoretical foundations with practical applications, working on innovative approaches to ensure the reliability and safety of AI systems in real-world scenarios.

Wendelin Böhmer serves as an Assistant Professor in the Algorithmics Group at TU Delft and co-directs the HERALD Delft AI Lab. After earning his PhD in computer science from the Technical University of Berlin, he worked as a Postdoctoral Researcher at Oxford University and Lecturer at St. Anne's College before joining TU Delft in September 2020. His research bridges the gap between inductive and deductive reasoning in artificial intelligence, with a particular focus on deep reinforcement learning. He contributes to several educational initiatives, including the AI Skills program where he teaches advanced concepts in unsupervised learning, deep learning, and reinforcement learning. His teaching portfolio includes comprehensive coverage of clustering techniques, dimensionality reduction, neural networks, and reinforcement learning fundamentals. Through his courses, students learn essential concepts such as k-means clustering, Principal Component Analysis, deep neural network architectures, and Q-learning applications. His teaching approach combines theoretical foundations with practical applications, helping students understand both the mathematical principles and real-world implementations of AI systems. He emphasizes hands-on learning through interactive exercises and practical projects, particularly in areas such as neural network training, reinforcement learning algorithms, and AI agent development.