Image Classification: Decision Trees & Random Forests
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Explores Decision Trees, from induction to pruning, emphasizing interpretability and automatic feature selection strengths, while addressing challenges like overfitting.
Explores uncertainty quantification and label error detection in deep learning for semantic segmentation, focusing on challenges and methods for error detection.
Explores techniques for delineation, including Hough transform, gradient orientation, and shape detection, emphasizing the importance of combining graph-based techniques and machine learning.
