What is Elements of AI?

Elements of AI is an online lesson that teaches the basics of artificial intelligence to anyone interested in learning about it. The study comes from the University of Helsinki and technology company Reaktor, with over 2,000 participants from 124 countries registered for the most recent class. Here's what you need to know about this unique and thorough course!

An Introduction to Elements of Artificial Intelligence

Elements of AI is a (MOOC) massive open online course introducing artificial intelligence. The study serves as an available educational resource that anyone can take for free. To take Elements of AI, you'll need a computer and internet access. There are no tests or quizzes—and no certificates awarded upon completion.

Learning to Program with Python

Python is one of the numerous straightforward programming languages to learn, so much so that you can pick it up just by working through Code Academy's simple lessons. Python has been named Programming Language of 2018 by TIOBE Index, a benchmark for language popularity. If you like to get into programming, even dip your toes with Python, these are some best resources for learning how to program with Python.

Machine Learning Fundamentals

What it Is and Why You Should Manage: Machine learning (ML) is an algorithm-based method for computers to learn without being explicitly programmed. ML uses several technologies, including artificial neural networks, hidden Markov models, and genetic algorithms. The foundation for modern machine learning was established in 1950 when Alan Turing published his paper Computing Machinery and Intelligence—which has become known as the Turing test—and predicted that machines would exhibit intelligent behaviour within 30 years.

Core Concepts of Deep Learning

Artificial Intelligence (AI) is a topic that's near and dear to many people, and it's making headlines everywhere. The only thing is that most people don't know what AI means. Artificial intelligence has existed for decades, but a more powerful form—called deep learning—has taken off in recent years with technologies like convolutional neural networks (CNNs). They understand what these complex technologies do and how they can be tough for newcomers.

Neural Networks Explained - From Perceptrons to Recurrent Nets

A Step-by-Step Visual Guide to Understanding Neural Networks. This guide will take you through simple neural networks and how they work using a visual approach. The depth at which you learn depends on how far you follow each section. However, even if you miss some areas, you can still get a good grasp of basic neural networks and apply what you've learned to other systems or expand upon it with new information as it's released.

Generative Models

This tutorial looks at generative models and explains how they work. We'll go over examples of GANs, Variational Autoencoders, and MAML. We'll also look at what these networks are used for today and some potential use cases in the future.

Other Machine Learning Models

Neural networks are one particular machine learning model, but many others. Elements of AI introduces you to four more common types: Bayesian models, decision trees, support vector machines and ensemble methods. The course then discusses how these models work and their limitations and advantages. Knowing a wide variety of models will help you decide which technique to apply to your problem when training a machine learning model.

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