126 Free Artificial Intelligence (AI) Courses, Ebooks, Videos and Papers

Free Content

1. Artificial Intelligence and Molecular Biology
   The current volume is an effort to bridge that range of exploration, from nucleotide to abstract concept, in contemporary AI/MB research.
2. Brief Introduction To Educational Implications Of Artificial Intelligence
   This book is designed to help preservice and inservice teachers learn about some of the educational implications of current uses of Artificial Intelligence as an aid to solving problems and accomplishing tasks.
3. Computers and Thought: A practical Introduction to Artificial Intelligence
   The book covers computer simulation of human activities, such as problem solving and natural language understanding; computer vision; AI tools and techniques; an introduction to AI programming; symbolic and neural network models of cognition; the nature of mind and intelligence; and the social implications of AI and cognitive science.
4. Encyclopedia: Computational intelligence
   Scholarpedia is a peer-reviewed open-access encyclopedia written and maintained by scholarly experts from around the world.
5. Ethical Artificial Intelligence
   A book by Bill Hibbard that combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence.
6. Foundations Of Computational Agents
   This book is published by Cambridge University Press, 2010
7. Golden Artificial Intelligence
   A cluster of pages on artificial intelligence and machine learning.
8. Modeling Agents with Probabilistic Programs
   This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning.
9. R2D3
   A website with explanations on topics from Machine Learning to Statistics. All helped with beautiful animated infographics and real life examples. Available in various languages.
10. Society of Mind
    Marvin Minsky’s seminal work on how our mind works. Lot of Symbolic AI concepts have been derived from this basis.
11. Stanford CS229 – Machine Learning
    This course provides a broad introduction to machine learning and statistical pattern recognition.
12. The Quest For Artificial Intelligence
    This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today’s AI engineers.

Code

1. AIMACode
   Source code for “Artificial Intelligence: A Modern Approach” in Common Lisp, Java, Python. More to come.
2. ExplainX
   ExplainX is a fast, light-weight, and scalable explainable AI framework for data scientists to explain any black-box model to business stakeholders.
3. FARGonautica
   Source code of Douglas Hosftadter’s Fluid Concepts and Creative Analogies Ph.D. projects.

Videos

1. A tutorial on Deep Learning
   Complex probabilistic models of unlabeled data can be created by combining simpler models. Mixture models are obtained by averaging the densities of simpler models and “products of experts” are obtained by multiplying the densities together and renormalizing.
2. Basics of Computational Reinforcement Learning
   In machine learning, the problem of reinforcement learning is concerned with using experience gained through interacting with the world and evaluative feedback to improve a system’s ability to make behavioral decisions. This tutorial will introduce the fundamental concepts and vocabulary that underlie this field of study.
3. Deep Reinforcement Learning
   In this tutorial I will discuss how reinforcement learning (RL) can be combined with deep learning (DL). There are several ways to combine DL and RL together, including value-based, policy-based, and model-based approaches with planning.
4. Intelligent agents and paradigms for AI
   This is a lecture video for the Carnegie Mellon course: ‘Graduate Artificial Intelligence’, Spring 2014.
5. The Unreasonable Effectiveness Of Deep Learning
   The Director of Facebook’s AI Research, Dr. Yann LeCun gives a talk on deep convolutional neural networks and their applications to machine learning and computer vision.

Learning

1. Deep Learning
   Yoshua Bengio, Ian Goodfellow and Aaron Courville put together this currently free (and draft version) book on deep learning. The book is kept up-to-date and covers a wide range of topics in depth (up to and including sequence-to-sequence learning).
2. Deep Learning. Methods And Applications Free book from Microsoft Research
   This book is aimed to provide an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks.
3. Getting Started with Deep Learning and Python
   In this blog post we’ll review an example of using a Deep Belief Network to classify images from the MNIST dataset, a dataset consisting of handwritten digits.
4. Introduction to Machine Learning
   Introductory level machine learning crash course.
5. Machine Learning Mastery
   Machine learning course that explains things clearly rather than writing everything as if it were an academic paper.
6. Neural Networks And Deep Learning
   Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning

General

1. Adversarial Networks
   Framework for generation.
2. Neural Turing Machine
   We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.
3. Representation learning
   Survey paper on representation methods.
4. Tensorflow
   Google’s large scale infrastructure project.

RNN structures

1. Deep Speech – Speech implementation.
2. End to End Memory networks – Facebook’s memory storage.
3. LTSM – Long term short term memory.
4. Memory Networks – On adding memory storage.
5. Neural Programmer – On adding basic artithmetic operations.
6. Spatial Transformer – DeepMind digit classification.

Word Vectors

1. Adaptive skip-gram
   Similar approach, with adaptive properties.
2. Infinite Dimensional Word Embeddings
   Describe a method for learning word embeddings with data-dependent dimensionality.
3. sense2vec
   On word sense disambiguation.
4. Skip Thought Vectors
   Word representation method.
5. word2vec
   On creating vectors to represent language, useful for RNN inputs.

Natural Language

1. LTSM over tree structures
   The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation. In this paper, we propose to extend it to tree structures, in which a memory cell can reflect the history memories of multiple child cells or multiple descendant cells in a recursive process.
2. Neural autocoder for paragraphs and documents
   LTSM representation.
3. Sequence to Sequence Learning
   Word vectors for machine translation.
4. Teaching Machines to Read and Comprehend
   DeepMind paper.

Convolutional Neural Nets

1. A Neural Algorithm of Artistic Style – Popular papeer.
2. DRAW – An RNN for image classfication.
3. Generative Adversarial Networks – Unsupervised learning to generate images.
4. ImageNet Classification – Popular paper.

Tutorials

1. K-Means with Tensorflow
2. LTSM RNN in Python
3. Tensorflow Tutorials

Datasets

1. DeepMind Q&A Corpus

Journals

1. AI & Society
2. AI Communications
3. AI Magazine
4. Annals of Mathematics and Artifical Intelligence
5. Applicable Algebra in Engineering, Communication and Computing
6. Applied Artificial Intelligence
7. Applied Intelligence
8. Artificial Intelligence
9. Artificial Intelligence for Engineering Design, Analysis and Manufacturing
10. Artificial Intelligence Review
11. Automated Software Engineering
12. Autonomous Agents and Multi-Agent Systems
13. Computational and Mathematical Organization Theory
14. Electronic Transactions on Artificial Intelligence
15. Evolutionary Intelligence
16. EXPERT—IEEE Intelligent Systems
17. IEEE Transactions Automation Science and Engineering
18. Intelligent Industrial Systems
19. International Journal of Intelligent Systems
20. International Journal on Artificial Intelligence Tools
21. Journal of Artificial Intelligence Research
22. Journal of Automated Reasoning
23. Journal of Experimental and Theoretical Artificial Intelligence
24. Journal of Intelligent Information Systems
25. Journal on Data Semantics
26. Knowledge Engineering Review
27. Minds and Machines
28. Progress in Artificial Intelligence

Organizations

1. AI Google
2. Association For The Advancement of Artificial Intelligence
3. Facebook AI
4. Google DeepMind Research
5. IBM Research
6. IEEE Computational Intelligence Society
7. Machine Intelligence Research Institute
8. Microsoft Research
9. Nvidia Deep Learning
10. OpenAI

Competitions

1. AI Challenge
2. MIT Battlecode

Newsletters

1. AI Digest
   A weekly newsletter to keep up to date with AI, machine learning, and data science.

Misc

1. AI Experiments with Google
   AI Experiments is a showcase for simple experiments that make it easier for anyone to start exploring machine learning, through pictures, drawings, language, music, and more.
2. AIResources
   Directory of open source software and open access data for the AI research community
3. AITopics
   Large aggregation of AI resources
4. Artificial Intelligence Subreddit
   Reddit’s home for Artificial Intelligence.
5. Open Cognition Project
   We’re undertaking a serious effort to build a thinking machine