[MUSIC PLAYING]
SPEAKER 1: Hello, world.
This is CS50.
And this is an Introduction
to Artificial Intelligence
with Python with CS50's own Brian Yu.
This course picks up where CS50 itself
leaves off and explores the concepts
and algorithms at the
foundation of modern AI.
BRIAN YU: We'll start
with a look at how AI
can search for solutions to
problems, whether those problems are
learning how to play a
game or trying to find
driving directions to a destination.
We'll then look at how AI
can represent information--
both knowledge that our
AI is certain about,
but also information and events about
which our AI might be uncertain.
Learning how to represent
that information,
but more importantly, how to
use that information to draw
inferences and new conclusions as well.
We'll explore how I can solve various
types of optimization problems-- trying
to maximize profits or minimize cost
or satisfy some other constraints--
before turning our attention to the
fast-growing field of machine learning,
where we won't tell our AI
exactly how to solve a problem.
But instead, give our AI
access to data and experiences
so that our AI can learn on its
own how to perform these tasks.
In particular, we'll look
at neural networks, one
of the most popular tools in modern
machine learning, inspired by the way
that human brains learn
and reason as well.
Before finally taking a look at the
world of natural language processing,
so that it's not just us humans learning
to learn how artificial intelligence is
able to speak, but also AI learning
how to understand and interpret
human language as well.
We'll explore these ideas and
algorithms and, along the way,
give you the opportunity to
build your own AI programs
to implement all of this and more.
This is CS50.
