LMS Invited Lecture Series 2022: The Mathematics of Deep Learning

LMS invited lectures by Gitta Kutyniok

• Lecture 1: Introduction to Deep Neural Networks
• Lecture 2: Deep Neural Networks: From Approximation to Expressivity
• Lecture 3: Deep Neural Networks: Analyzing the Training Algorithm
• Lecture 4: Deep Neural Networks: The Mystery of Generalization
• Lecture 5: Deep Neural Networks: Opening the Black Box via Explainability Methods
• Lecture 6: Deep Neural Networks: Towards Robustness
• Lecture 7: Deep Neural Networks: Can Deep Neural Networks be Provably Fair?
• Lecture 8: Inverse Problems meet Deep Learning: Optimal Hybrid Methods
• Lecture 9: Partial Differential Equations meet Deep Learning: Beating the Curse of Dimensionality
• Lecture 10: Mathematical Foundations of Deep Learning: Potential, Limitations, and Future Directions

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  Accompanying lectures

• Peter Bartlett (UC Berkeley)
• Weinan E (Princeton University)
• Klaus-Robert Müller (TU Berlin)
• Rebecca Willett (University of Chicago)

Further information and Registration

The Invited Lecture Series

The annual Invited Lecturers scheme aims to bring a distinguished overseas mathematician to the United Kingdom to present a small course of about ten lectures spread over a week. Each course of Invited Lectures is on a major field of current mathematical research, and is instructional in nature, being directed both at graduate students beginning research and at established mathematicians who wish to learn about a field outside their own research specialism.