Part 0 - Introduction to AI (Course Content)

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Table of Contents:

A) Introduction to Artificial Intelligence

A.1) Course Objectives

Welcome, everyone, to an introduction to Artificial Intelligence with Python.

My name is Brian Yu, and throughout this course, we’ll dive into various concepts, techniques, and algorithms that lie at the heart of artificial intelligence (AI).

When you see a computer perform tasks that seem "intelligent"—whether it's identifying faces in photos, beating humans at complex games, or understanding human language—these are all examples of AI at work.

In this course, we aim to explain how AI functions, how it makes decisions, and what enables these impressive feats.

A.2) Course Content

a) Intro to "Search Problems" with AI

We'll begin our journey with a look at search problems.

These problems occur when we want our AI to search for the best solution to a problem.

For example, figuring out the shortest route between two points (as you might do with GPS),

or deciding the best move in a game such as Tic-Tac-Toe,

are both examples of search problems.

b) How AI can acquire "Knowledge"

After that, we'll take a look at knowledge.

Ideally, we want our AI to:

• to be able to know information,
• to be able to represent that information,
• and more importantly, to be able to draw inferences from that information. And be able to use the information it knows and draw additional conclusions.

So we'll talk about how AI can be programmed in order to do just that.

https://es.wikipedia.org/wiki/Modus_ponendo_ponens

c) How AI deals with "Uncertainty"

Then we'll explore the topic of uncertainty.

Talking about ideas of, what happens if a computer isn't sure about a fact but maybe is only sure with a certain probability?

So we'll talk about some of the ideas behind probability and how computers can begin to deal with uncertain events in order to be a little bit more intelligent in that sense, as well.

d) About "Optimization"

After that, we'll turn our attention to optimization.

Problems of when the computer is trying to optimize for some sort of goal, especially in a situation where there might be multiple ways that a computer might solve a problem, but we're looking for a better way or, potentially, the best way if that's at all possible.

e) About Machine "Learning"

Then we'll take a look at machine learning, or learning more generally.

In looking at how when we have access to data, our computers can be programmed to be quite intelligent by learning from data and learning from experience, being able to perform a task better and better based on greater access to data.

So your email, for example, where your email inbox somehow knows which of your emails are good emails and whichever emails are spam.

These are all examples of computers being able to learn from past experiences and past data.

f) About "Neural Networks"

We'll take a look, too, at how computers are able to draw inspiration from human intelligence, looking at the structure of the human brain and how neural networks can be a computer analog to that sort of idea.

And how, by taking advantage of a certain type of structure of a computer program, we can write neural networks that are able to perform tasks very, very effectively.

g) About Natural "Language" Processing

And then finally, we'll turn our attention to language.

Not programming languages, but human languages that we speak every day.

And taking a look at the challenges that come about as a computer tries to understand natural language and how it is some of the natural language processing that occurs in modern artificial intelligence can actually work.

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Z) 🗃️ Glossary