Intelligent Image and Graphics Processing

This course introduces the basic principles and methods of machine learning for graphics and images, the applications for intelligent navigation and guidance of unmanned aerial vehicle. The main contents include models of machine learning, learning, and optimal inference, application of machine learning for segmentation, target identification/tracking/classification for graphics and images, and 3D reconstruction, etc. In addition, examples are provided for better understanding the application of method and theory to solve practical problems. The teaching methods of this course include theoretical teaching, discussion of system design and experiments.

Contents

• Introduction
• Traditional machine learning methods
  • kmeans
  • Support Vector Machine
  • Neural Network
• Deep Learning
  • Restricted Boltzman Machine
  • Convolutional Neural Networks
  • Deep Reinforcement Learning
• Structure from Motion
• Simultaneous Localization and Mapping (SLAM)
  • SLAM introduction
  • General SLAM

The slides of this course can be download at: http://www.adv-ci.com/download/course/intelligent_igp/ (It should be note that some of the slides are from network.)

Learning Materials (Awesome Series)

• Awesome Cmputer Vision
• Awesome Deep Learning
• Awesome - Most Cited Deep Learning Papers
• Awesome Deep Vision
• Awesome 3D Reconstruction

Links

Some excellent lectures are listed below:

• Machine Learning

• CS229: Machine Learning

• CS 20: Tensorflow for Deep Learning Research

• CS 294: Deep Reinforcement Learning, UC Berkeley

• Deep Learning Book (CN)

• Machine Learning Crash Course with TensorFlow APIs

• Nvidia DLI

• Introduction to Machine Learning

• Computer Vision @ ETHZ

• SFMedu: A Structure from Motion System for Education

• scene understanding of computer vision

• Autonomous Navigation for Flying Robots

• Multiple View Geometry

• Deep Learning for Self-Driving Cars

• 史上最全TensorFlow学习资源汇总

• Oxford Deep NLP 2017 course （牛津大学xDeepMind自然语言处理）