Machine Learning and Artificial Intelligence: Karl

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Related lectures (32)

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Systems of Linear Equations and Matrices

Covers the definition and solution of linear equations with real coefficients in 2D and 3D space.

Signals and Systems I: Symmetry Relations and Modulation

Explores symmetry relations, modulation, Fourier transform properties, and integration methods in signals and systems.

Gradient-Based Algorithms in High-Dimensional Learning

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.

Data-Driven Modeling of Haemodynamics in Vascular Flows

Explores data-driven modeling of haemodynamics in vascular flows, focusing on computational challenges, reduced order modeling, FSI problems, and neural network applications.

Engineering the Future: Nano-Tera

Explores the Swiss Research Program Nano-Tera and its impact on engineering multi-scale systems for health, security, energy, and the environment.

Engineering in the Age of AI: Innovations and Challenges

Examines the transformative impact of AI on engineering disciplines and the associated challenges.

Artificial Intelligence: Foundations and Machine Learning

Covers the foundations of artificial intelligence, machine learning, the data revolution, and research examples in AI and machine learning.

Thermodynamics and Energetics I

Explores fundamental thermodynamics concepts, laws, energy transfer, and system analysis.

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Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.