Artificial Intelligence and Machine Learning Resources
AI and ML are broad categories with somewhat imprecise, unsettled, and evolving descriptions.
Achieving a basic, technical level of understanding of how AI and ML work is critical for legal and policy officers to incorporate the unique demands of these systems into their governance models. These resources range from news articles to on-line technical training to provide the desired level of overview.
Future of Privacy Forum Publishings
Covering the technological basics of AI and ML systems at a level of understanding useful for non-programmers, and addressing certain privacy challenges associated with the implementation of new and existing ML-based products and services.
Interactive Explanations and Courses
Machine Learning 101 - A comprehensive overview of AI and machine learning with numerous resources for additional research.
Intro to Machine Learning - A detailed, video-based, interactive course into ML concepts. Prerequisites include strong algebra skills as well as proficiency in programming basics, including Python, using Tensor Flow.
Making Sense of Artificial Intelligence - This A-Z guide offers a series of simple, bite-sized explainers to help anyone understand what AI is, how it works and how it's changing the world around us.
Udacity, Intro to Artificial Intelligence - A detailed course on the basic concepts of AI.
University of Helsinki, Elements of Artificial Intelligence - Free online course in 6 parts, six weeks based on5 hours per week, but can be skimmed for specific areas and interests.
Blog, Brandon Rohrer, Data Science and Robots - A series of posts and videos exploring a detailed breakout of topics about how Machine Learning works, reviewed types of ML, uses, and an overview of Artificial Intelligence applications
Trailhead, Artificial Intelligence Basics - Two fifteen minute courses on the fundamentals and applications of Artificial Intelligence.
Harvard University, ICML Tutorial - Slide show demonstrating types of Machine Learning, interpretability, and model selection process. Highly Technical.
Machine Learning Mastery, A Tour of Machine Learning Algorithms - A discussion about the various types of algorithms used in Machine Learning.
Medium, Machine Learning for Humans - An extensive, five-course lesson on Machine Learning
3Blue1Brown, But What *Is* A Neural Network? - A three-part video series explaining deep learning, gradient descent, and backpropagation.
R2D3, A Visual Introduction to Machine Learning - Demo of how to apply various statistical methodologies to differentiate homes in NYC v. SF.
Microsoft, Professional Program for Artificial Intelligence - Ten extensive, 8-16 hour online courses, ranging from a broad overview of Artificial Intelligence to instructions on how to code for machine learning.
Google, AI Experiments - A showcase for simple experiments that make it easier for anyone to start exploring machine learning, through pictures, drawings, language, music, and more.
FAT/CV, Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision at CVPR 2020 - workshop examining the ethical implications of deploying this technology.
Fierce Electronics, What is Artificial Intelligence (AI)? - provides a history of AI, the future of AI, along with several educational resources.
News, Reports, and Other Media
Brookings, Darrel West, What is Artificial Intelligence - Argues that AI systems have three qualities that constitute the essence of Artificial Intelligence: intentionality, intelligence, and adaptability and why it is important to make sure each accords with basic human values
Medium, Ways to Think About Machine Learning - A conceptual discussion on the terms of Artificial Intelligence, Machine Learning, neural networks, as well as the history of the tech, and the differences, applications, and future use cases for each
Detalku, Your Guidebook: Machine Learning Basics - Downloadable guide to basic concepts; requires registration to download
The Fourth Industrial Revolution: A Primer on Artificial Intelligence - A comprehensive rundown of Artificial Intelligence, Machine Learning, and neural networks
Artificial Intelligence: Understanding the Hype - A very short article explaining the basic terms of AI
Logistic Regression: Detailed Overview - Detailed (mathematically explicit) explanation of regression
Gartner, The CIO's Guide to Artificial Intelligence - A discussion of the current applications of Artificial Intelligence
UX Collective, The Beginner's Guide to Understanding Artificial Intelligence - A collection of resources, including interactive examples and movies, explaining the basics of AI
Wall Street Journal, What Machine Learning Can and Cannot Do - An overview of the tasks ML is particularly suited to, including criteria to help distinguish tasks in which it is likely to be successful.
Wait But Why, The AI Revolution: The Road to Superintelligence - A groundbreaking and often-cited article about ASI and the singularity.
A Beginner's Guide to Artificial Intelligence, Machine Learning, and Cognitive Computing - A fairly deep dive into some of the concepts of AI, including perceptrons, backpropagation, and cognitive computing
Security and Artificial Intelligence FAQ - Instructional Guide from IBM (downloadable) in Q&A format; includes security aspects
Quartz, The Quartz Guide to Artificial Intelligence - A very basic overview of AI, including glossary of important terms
Wired, Machine Learning Versus AI: What's the Difference? - A simple explanation of the difference between AI and machine learning
Futurism, This is What a True Artificial Intelligence Really Is - A article about the ambiguity and misperceptions of AI
Campaign, Introduction to Algorithms, Machine Learning, and AI - A basic introduction to AI
Robotics Business Review, What is Artificial Intelligence? Understanding 3 Basic AI Concepts - An article distinguishing between machine learning, deep learning, and neural networks. Paywall after 3 visits
Towards Data Science
"WTH Does A Neural Network Even Learn?" - A Newcomer's Dilemma - An article discussing the black box problems of hidden layers within a neural network
The 5 Clustering Algorithms Data Scientists Need to Know - A detailed discussion about the five different types of clustering used in machine learning
MIT Review, The GANfather: The man who's given machines the gift of imagination - Explanation of "Generative Adversarial Networks" (a kind of neural network), with examples
TNW ("The Next Web"), A Beginner's Guide to AI: Neural Networks - Detailed discussion and explanation of how artificial neural networks work, focusing on 3 basic types: general adversarial networks; convolutional neural networks; recurrent neural networks
Datalku, Machine Learning Explained: Algorithms are Your Friend - Definitions and useful graphic of the most common types of algorithms and machine learning models. (Connected to DataIku Basic Guide, listed above)
Deep Misconceptions About Deep Learning - Fairly technical discussion of how deep learning processes work, and ways to implement techniques with greater success
The Best Machine Learning Resources - An addendum to the Machine Learning for Humans guide (listed above) which has dozens of links to AI learning resources
Supervised Machine Learning: Regression v. Classification - Part of an excellent series on the technical differences of various ML algorithms
HackerEarth, Artificial Intelligence 101: How to Get Started - A large list of resources related to AI and bots
Amazon, What is Artificial Intelligence - A brief overview of the concepts of AI
Facebook, Artificial Intelligence, Revealed - A broad synopsis of AI, including several explanatory videos
Bernard Marr & Co., What is the Difference Between Artificial Intelligence and Machine Learning? - Article explaining the difference between AI and ML, including a history of the origin of AI
Intel, The Difference Between AI and Machine Learning - A video explaining the difference between AI and ML
Brave New Coin, Bias in Artificial Intelligence and the Importance of Independent Data - Article discussing the problem of biased data and AI
DataScience, Introduction to K-means Clustering - Technical guide to algorithmic clustering
IAPP, The Privacy Pro's Guide to Explainability in Machine Learning - A discussion of the accuracy vs. interpretability tradeoff in machine learning
Toward Responsible AI for the Next Billion Users - List suggested research areas tp facilitate discussion on implicit beliefs, biases, and issues that may be instituted in AI
Zendesk, A Simple Way to Understand Machine Learning vs. Deep Learning - A discussion about the difference between AI and machine learning, including a discussion about deep learning vs. machine learning and the relationship between AI and Big Data.
Kristian Hammond, Practical Artificial Intelligence for Dummies
AI Now, Algorithmic Impact Assessments: A Practical Framework for Public Agency Accountability - A policy paper providing public agencies a practical framework to assess automated decision systems and to ensure accountability.
CIPL, Artificial Intelligence and Data Protection in Tension - Brief overview of AI and ML technologies, and review of the conflicts with traditional privacy principles.
Ethem Alpaydin, Machine Learning
Michael Nielsen, Neural Networks and Deep Learning
TutorialsPoint, Artificial Intelligence Tutorial
PDF: Basic pdf file that’s shorter than a book, but more in depth than an article. In terms of content, it serves as much more of an overview for individuals who are interested in the development side of AI. However, it provides good descriptions of multiple areas/components inside AI, complete with terminology, examples of application in the real world, and tracking how the machine receives input and produces its output.
Zenodo, Algorithm Impact Assessment: Fairness, Robustness and Explainability in Automated Decision-Making - A workshop discussing key issues regarding algorithm impact assessment.
Norwegian Data Protection Authority, Artificial Intelligence and Privacy - Overview of AI concepts and a lengthy discussion of the privacy harms associated with it
ICO, Big Data, Artificial Intelligence, Machine Learning, and Data Protection - A 114 page report on the privacy implications of Big Data and AI, specifically as it relates to the GDPR
Royal Society, Machine Learning: The Power and Promise of Computers That Learn by Example - A 138 page report on the social implications of machine learning in the UK. Includes the detailed summary of the technology
Richard S. Sutton & Andrew G. Barto, Reinforcement Learning: An Introduction
Sean Gerris, How Smart Machines Think
Terrence J. Sejnowski, The Deep Learning Revolution
Matthew Sadler & Natasha Regan, Game Changer: AlphaZero's Groundbreaking Chess Strategies and the Promise of AI
Andriy Burkov, The Hundred-Page Machine Learning Book
Office of the Victorian Information Commissioner Closer to the Machine: Technical, Social, and Legal Aspects of AI