| Students are able to practically apply geometric deep learning (GDL) techniques to non-Euclidean data structures such as graphs and manifolds. They gain hands-on experience in implementing, optimizing, and evaluating geometric deep learning models, using mathematical tools like Fourier transforms, wavelet transforms, and symmetry-based methods.
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- Implementing Graph Neural Networks (GNNs) and Manifold Learning (k3)
Students can design, implement, and train GNNs and other manifold-based learning models to process structured and relational data effectively.
- Applying Symmetries and Invariances in Model Design (k5)
Students are able to integrate principles of symmetry, equivariance, and invariance into deep learning models to improve robustness and generalization.
- Utilizing Fourier and Wavelet Transforms in Deep Learning (k4)
Students can apply Fourier and wavelet transforms to analyze data in geometric deep learning, optimizing data representation for structured and irregular domains.
- Optimizing Geometric Deep Learning Models (k5)
Students are capable of fine-tuning hyperparameters and optimization strategies for geometric deep learning models, improving their efficiency and predictive performance.
- Developing and Evaluating GDL Applications (k5)
Students can implement real-world applications of geometric deep learning, such as drug discovery, social network analysis, and 3D object recognition, and evaluate model performance using appropriate metrics.
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Students know how to implement geometric deep learning models, including GNNs, manifold learning, and symmetry-based architectures. They apply mathematical transformations and optimization techniques to improve model performance in structured data applications.
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