GNR 652 - Machine Learning for Remote Sensing 1

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
Total credits : 6 credits - Lecture
Type : Theory
Duration : Full Semester