Course Details

Course details of EE5177
Course NoEE5177
Course TitleMachine Learning for Computer Vision
Course Content1. Probability: Common probability distributions such as Gaussian, Bernoulli, Dirichlet, etc.. Fitting probability models.2. Machine Learning models and inference:Regression models such as linear regression, Bayesian regression, nonlinear regression, sparse linear regression.Classification models such as logistic regression, support vector machine, relevance vector machine, classification tree.3. Graphical models:Directed and undirected graphical models; models for trees; Markov random fields; Conditional Markov fields.4. Image pre-processing:Per-pixel transformation; interest point detection and description; dimensionality reduction.5. Multi-view geometry:Pinhole camera; single view geometry; Projective transformation; Stereo and epipolar geometry; Multi-view reconstruction6. Models for vision:Models for shape; Models for style and identity; temporal models; models for visual words
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