This is going to be a list of resources for learning the required topics to be considered knowledgeable in the field of artificial intelligence. This will be everything I can find, including textbooks, researchers, papers, courses, video series, and notes. Some of these books can be put in other categories, but I just put them in what I would see most.




  • Learning how to program
  • Python
  • R






Data Science


Machine Learning


Deep Learning


Cognitive Thinking




Artificial Intelligence


Researchers and People to know



  • Bayesian Reasoning and Machine Learning – David Barber
  • Where Do Features Come From? Geoffrey Hinton
  • Modeling Documents With a Deep Boltzmann Machine – Geoffrey Hinton, Nitish Srivastava, and Ruslan Salakhutdinov
  • Distilling the Knowledge in a Neural Network – Geoffrey Hinton, Oriol Vinyalis, and Jeff Dean
  • Grammar as a Foreign Language – Hinton plus others
  • Information Science and Statistics – Christopher Bishop
  • Information Theory, Inference, and Learning Algorithms – David MacKay
  • An Introduction into Statistical Learning with Applications in R
  • Dropout: A Simple Way to Prevent Neural Networks from Overfitting – Toronto CS
  • Machine Learning – Peter Flach
  • Building Machine Learning Systems with Python – Willi Richert
  • To Recognize Shapes, First Learn to Generate Images – Hinton
  • Deep Learning – LeCun, Bengio, Hinton
  • A Fast Learning Algorithm for Deep Belief Nets – Hinton, Teh, Osindero
  • Speech Recognition with Deep Recurrent Neural Networks – Hinton, Mohammed, Graves
  • Reducing the Dimensionality of Data with Neural Networks – Hinton, Salakhuditinov
  • Superintelligence Paths Dangers Stragies – Bostrom