Machine Learning for Computer Vision class (Winter 2017-2018) - YouTube

Machine Learning for Computer Vision Course Overview

• Course Title: Machine Learning for Computer Vision
• Instructor: Prof. Fred Hamprecht
• Institution: HCI, Heidelberg University
• Term: Winter 2017/2018
• Course Syllabus:
  • Introduction
  • Undirected Probabilistic Graphical Models: Includes MAP & Priors, Markov Random Fields (MRF), Gibbs Sampling, MRF as Integer Linear Programs, Tree-Shaped MRF, Belief Propagation, and Gaussian MRF.
  • Neural Networks: Covers Perceptrons, Back Propagation, Deep Learning introduction and architectures, Natural Gradient Optimization, and combining Graphical Models with Neural Networks.
  • Directed Probabilistic Graphical Models: Covers Reinforcement Learning, Policy Gradient, and Robotics.
• Resources: Handwritten lecture notes are provided via the HCI Heidelberg website.