Course content

• Introduction to remote sensing image analysis
• Math refresher - probability, linear algebra, calculus
• Introduction to machine learning - an overview, learning theory
• Supervised learning - regression, effects of regularization, loss function, gradient based parameter optimization
• Supervised learning - classification: basic idea, nearest neighbor based classifier
• Supervised learning - classification: probabilistic classifiers, parameter estimation techniques (maximum likelihood, maximum a posteriori), Gaussian mixture models and expectation maximization
• Supervised learning - classification: Introduction to graphical models (Bayesian networks, Markov random fields, Conditional random fields, Hidden Markov Model)
• Supervised learning - classification: support vector machines (soft and hard margin, idea of kernel functions)
• Supervised learning - neural networks for classification and regression (multi-layer perceptron model), derivation of back-propagation, radial basis function networks
• Unsupervised classification - clustering and density estimation, k-means clustering, graph-cut based clustering, mean-shift clustering and kernel density estimate
• Dimensionality reduction techniques - idea of feature transformation and feature selection, principal component analysis, independent component analysis, neighborhood component analysis, auto-encoder (basic, denoising, sparse)
• Semi-supervised learning - probabilistic methods, transductive SVMs, graph based semi- supervised learning
• Weakly supervised learning - idea of weak supervision, zero-shot, few-shot learning
• Introduction to deep learning Convolutional networks - architectures in detail, CNN for image classification, semantic image segmentation, and object detection
• Recurrent networks - basic RNN, LSTM, GRU

References