Artificial Intelligence

In this series, you will learn the logic behind AI by tracing its history and examining how it’s being used today.

AI is everywhere -- autocompleting texts on our cellphones, beating us at video games, driving cars, and more! Today we're going to explain what AI can (and can't) do right now.

Today we’re going to teach John Green Bot how to tell the difference between donuts and bagels using "supervised learning"

Today, we're going to combine the artificial neuron we created last week into an artificial neural network.

Today we’re going to talk about how neurons in a neural network learn by getting their math adjusted.

In our first lab, we’re going to program a neural network to recognize handwritten letters.

We’ll focus on the performing unsupervised clustering, specifically K-means clustering.

One of the most common ways we interact with computers is through language. Today, we're going to talk about Natural Language Processing.

Today, we're going to code a program that takes a one-word prompt and then completes the sentence that sounds like something John Green would say.

Reinforcement learning is particularly useful in situations where we want to train AIs.

Symbolic AI is different from the modern neural networks we've discussed so far. It represents problems using symbols and then uses logic to search for solutions.

Today, we're going to take a look at the role of AI in overcoming three key challenges in the field of robotics: localization, planning, and manipulation.

Today, we’re going to walk you through creating a Tic Tac Toe bot that uses the minimax algorithm to become undefeatable.

Today we create a game and then build an AI to destroy it. We'll use machine learning techniques such as an evolutionary neural network.

Human-AI teams allow us to fill in each others weaknesses leveraging human creativity and insight with the ability to perform rote manual tasks and synthesize lots of information.

Today we’ll learn about recommender systems which form the backbone of the content we see online from video recommendations on YouTube and Netflix.

Today, we’re going to build a movie recommender systems to find that perfect movie.

Let's talk about AI search engines, which are AI systems that help us find what we’re looking for.

Today, we're going to talk about five common types of algorithmic bias we should pay attention to. Bias can become a problem if we don't acknowledge exceptions to patterns or if we allow it to discriminate.

Today, in our final lab, Jabril tries to make an AI settle the question once and for all, "Will a cat or a dog make us happier?"

Today, in our final episode of Crash Course AI, we're going to look towards the future.