Bostrom, Nick. Superintelligence. Oxford University Press, 2014
Nick Bostrom describes the actual status and current thinking about the possibility and dangers of a superintelligence.
Goodfellow, Ian, Bengio, Yoshua, Courville, Aaron. Deep Learning, MIT Press, 2016. Website: www.deeplearningbook.org
Excellent book about the actual status of deep learning including a profound mathematical foundation.
Jurafsky, Dan and Martin, James H. Speech and Language Processing, 3rd Edition Draft: https://web.stanford.edu/~jurafsky/slp3/.
Dan is Chair and Professor at Stanfort Universitry, James is professor at Boulder University. This is a leading book on Speech and Language Processing. The 3rd edition is in draft, not all chapters are available yet. All available chapters can be downloaded as text (pdf) or slides (pdf or ppt) and can be used freely.
Russell, Stuart and Norvig, Peter: Artificial Intelligence. A Modern Approach. Third Edition. Pearson 2010. Website: aima.cs.berkeley.edu
Excellent introduction into the huge field of artificial intelligence. Most used textbook for AI classes.
Public Lecture: Deep Learning and the Future of Artificial Intelligence – held by Yann LeCun: Great introductory lecture about the history, the actual state and the possible future of Neural Networks with focus on Convolutional Neural networks (CNN). (link).
CS229 – Machine Learning – held by Andrew Ng: 20 lectures from the University of Stanford. Recordings available on YouTube (lecture 1).
CS231n – Convolutional Neural Networks for Visual Recognition: 10-weeks course from the University of Stanford. Much of the background and materials of this course is drawn from the ImageNet Challenge. Video recordings of the lectures from winter 2016 are available on YouTube. Some of the lectures have been held by Andrej Karpathy (actually working at Tesla). The course project reports of the students are also available online.
CS224N – Natural Language Processing with Deep Learning: 18 lectures from the University of Stanford. Recordings available on YouTube (link to the recordings).
distill.pub: A website to publish machine learning research work in hight quality. Led by the editors Shan Carter and Chris Olah from the Google Brain Team. Also the stearing committee is first class with members like Ian Goodfellow, Joshua Bengio and Andrej Karpathy. The articles are thoroughly reviewed and the layout is web-native with beautiful animated gifs to illustrate the content. Unfortunately, only a few articles are published so far (the last from April 2017).
arXiv.org: A web-based public library of scientific papers managed by the Cornell University. Actual papers of different topics (like physics, mathematics, computer science) are public available and can be dowloaded as pdf. Papers are reviewed before publication. A lot of important machine learning papers are published and available here. The papers are identified and referenced by article-IDs.
aiindex.org: This is a yearly study on the actual status of Artificial Intelligence. Is is a project within the Stanford 100 Year Study on AI. It is an initiative to track, collate, distill and visualize data relating to the actual status of artificial intelligence. A yearly study is published with leading AI researchers and representatives from the industry as authors.
Journal of Machine Learning Research (www.jmlr.org): The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.