Collection of Materials related to Machine Learning

Machine learning relevant research has been so prevalent in a recent decade, but now it seems gradually slowing down. There are so many excellent online materials to boost our study on it. Here I just list a few representatives I have learned, aiming to help those who also want to gain the basic knowledge of machine learning.

• Machine learning
  • CS229: Machine Learning Probably, this is one of the most famous online courses about machine learning, given by Andrew Ng from Stanford University. accompanying notes from github
  • 10-601 Machine Learning, by Tom Mitchell and Maria-Florina Balcan from Carnegie Mellon University.
  • Statistical Learning, by Stanford University professors Trevor Hastie and Rob Tibshirani.
  • Github Tutorial on Machine Learning
• Deep learning-CNN

  • CS231n: Convolutional Neural Networks for Visual Recognition. This is absolutely very admiring open course with a lot of conctrete examples and hands-on coding samples, which are prepared by Prof. FeiFei Li’s group from Stanford University. From this course, we can have a bite on deep learning algorithms, starting from the implementation of CNNs into computer vision.

  • MIT Deep Learning and Artificial Intelligence Lectures, Lex Fridman from MIT. He is an amazing guy and I listen a lot when I exercise to his podcast, which is about talking with celebrities from various background and often inspiring.

• Reinforcement learning
  • Reinforcement Learning, by Richard S. Sutton from University of Alberta, Solutions to his book