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This intensive five-day course delves into the theoretical and practical aspects of Tensor Networks specifically for Machine Learning applications. You will explore technical details, programming techniques, and real-world applications through lectures, tutorials, and exercises. The course is designed for individuals with a solid foundation in machine learning, statistical modeling, mathematics, and computer science, and requires programming experience, ideally in Python. Participants will complete the course by submitting a report on the topics covered. This is a short, focused course, offering 2.5 ECTS points.
This course offers a focused and intensive introduction to modern computational tools and techniques designed for handling and analyzing massive datasets. The primary emphasis is on providing practical, hands-on experience with these tools. Upon completion, students will be equipped to select or develop algorithms for specific data science tasks, implement and apply these algorithms to real-world problems, and critically evaluate technologies for parallel and distributed computing. The course also covers how to analyze the scalability of computational methods and clearly articulate the reasoning behind design and development choices.