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Computer Vision
Chapters

1Foundations of Image Formation

2Low-Level Image Processing

3Feature Detection and Representation

4Classical Computer Vision: Geometry and Matching

5Machine Learning for Vision

Feature Vectors and Data PreparationNearest Neighbors and k-NNLinear Models: SVM and Logistic RegressionDecision Trees and Ensemble MethodsDimensionality Reduction and PCAClustering and Unsupervised MethodsMetric Learning and Similarity MeasuresEvaluation Metrics for ClassificationCross-validation and Model SelectionRepresentational Transfer Learning

6Deep Learning Foundations for Vision

7Advanced Deep Architectures and Vision Transformers

8Object Detection, Localization, and Recognition

Courses/Computer Vision/Machine Learning for Vision

Machine Learning for Vision

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Introduce supervised and unsupervised learning methods applied to vision: classic classifiers, clustering, representation learning, and evaluation metrics.

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Representational Transfer Learning

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