| Students are able to apply geometric deep learning (GDL) techniques to analyze and process data in non-Euclidean domains such as graphs, manifolds, and structured data. They can implement and optimize deep learning models using geometric principles like symmetry, invariance, relational inductive biases, and mathematical transformations, enabling advanced applications in areas such as drug discovery, social network analysis, and 3D object recognition.
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- Applying Geometric Deep Learning to Non-Euclidean Data (k5)
Students are able to apply deep learning techniques to complex data structures like graphs and manifolds, extending neural network capabilities beyond traditional Euclidean domains.
- Implementing Graph Neural Networks and Manifold-Based Learning (k3)
Students can design and implement graph neural networks (GNNs) and other manifold-based learning methods to process structured and relational data effectively.
- Utilizing Symmetries and Invariances in Model Design (k5)
Students are able to incorporate principles of symmetry, equivariance, and invariance into deep learning architectures to improve model robustness and efficiency.
- Applying Fourier and Wavelet Transforms in Geometric Learning (k4)
Students can use mathematical transformations such as Fourier and wavelet transforms to analyze and optimize data representations in geometric deep learning models.
- Optimizing Deep Learning Models for Structured Data (k5)
Students are capable of fine-tuning and optimizing geometric deep learning models, improving generalization and computational performance in applications such as protein structure prediction and 3D object recognition.
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Students know of geometric deep learning principles, including graphs, manifolds, and non-Euclidean data representations, as well as their applications in AI. They are able to leverage mathematical tools such as Fourier and wavelet transforms, symmetries, and relational inductive biases to enhance deep learning models for structured data analysis.
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