AI Skills: Machine Learning Fundamentals
Master key ML concepts: unsupervised learning, neural networks & reinforcement learning - practical techniques for real-world AI solutions.
Master key ML concepts: unsupervised learning, neural networks & reinforcement learning - practical techniques for real-world AI solutions.
This comprehensive course delves into fundamental machine learning concepts and their practical applications. Students will explore unsupervised learning techniques for analyzing unlabeled data, including clustering methods and dimensionality reduction. The course covers deep learning principles, focusing on neural network architecture and training methodologies. Additionally, students will learn reinforcement learning fundamentals, understanding how AI agents interact with environments through hands-on exercises. The curriculum emphasizes practical implementation using Python, with interactive exercises designed to solidify theoretical concepts. Led by TU Delft's machine learning experts, the course provides a balanced mix of theoretical knowledge and practical skills, preparing students to apply these techniques to real-world engineering challenges.
Instructors:
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What you'll learn
Implement clustering algorithms including k-means and hierarchical clustering
Apply dimensionality reduction techniques using Principal Component Analysis
Build and train deep neural networks for classification and regression tasks
Understand reinforcement learning concepts and their real-world applications
Develop practical skills in Python for machine learning implementation
Master the fundamentals of neural network architecture and training
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 comprehensive machine learning course covers four main areas: clustering techniques for pattern identification in unlabeled data, dimensionality reduction methods including PCA for feature extraction, deep learning fundamentals focusing on neural network architecture and training, and reinforcement learning principles for AI agent interaction. The course combines theoretical concepts with practical implementation, providing hands-on experience through interactive exercises. Each topic is presented from various perspectives by TU Delft's machine learning experts, ensuring a well-rounded understanding of both fundamental concepts and their practical applications.
Introduction
Module 1
Clustering
Module 2
Dimensionality Reduction
Module 3
Introduction to Deep Learning
Module 4
Introduction to Reinforcement Learning
Module 5
Fee Structure
Instructors
4 Courses
Expert in Machine Learning Education and Learning Curve Research
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.
3 Courses
Pioneer in AI-Based Digital Infrastructure and Railway Engineering
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.
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