Learn about artificial intelligence without complication.

Learn about artificial intelligence without complication.

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What will be covered throughout the course?

A little video introducing some aspects of the course.

Only a file containing all the references used to make this course.

Some general aspects of Artificial Intelligence.

In this lesson, let's make a brief introduction to artificial intelligence, briefly telling its story and its definitions.

In this lesson, you'll learn what the turing test is, how it works, and what components are required to pass the test.

In this lesson, you'll learn about rational agents, rationality measures, and the PEAS framework.

In this section, you'll start learning the first steps to solve simple AI problems.

In this lesson, let's make a brief introduction about graphs, their properties, and types.

In this lesson, you'll learn about the adjacency arrays.

An exercise about adjacency arrays.

An exercise about adjacency arrays.

In this lesson, let's deepen the study on artificial intelligence knowing the breadth-first search algorithm.

An exercise about breadth-first search.

An exercise about breadth-first search.

In this lesson, you'll learn about the depth-first search algorithm.

An exercise about depth-first search.

Heuristics is a cognitive process, used in non-rational decisions. It’s a strategy that ignores part of the information in order to make the choice easier and faster.

In this lesson, will be presented the best-first algorithm for heuristic search.

An exercise about best-first.

In this lesson, will be presented the A* algorithm for heuristic search.

An exercise about A*.

In this section you'll learn algorithms used to perform searches on trees, revealing their minimum weights.

In this lesson, you'll learn how to solve minimal tree searches using the Kruskal algorithm.

An exercise about Kruskal.

In this lesson, you'll learn how to solve minimal tree searches using the Dijkstra algorithm.

An exercise about Dijkstra.

An exercise about Dijkstra.

In this lesson, you'll learn how to solve minimal tree searches using the prim algorithm.

An exercise about prim.

In this section, you'll learn about the three types of minimax searches.

In this lesson, you'll learn how to solve problems involving the minimax search with only 2 players.

An exercise about minimax with 2 players.

In this lesson, you'll learn how to solve problems involving the minimax search with more than 2 players.

An exercise about minimax with more than 2 players.

In this lesson, you'll learn how to solve problems involving the minimax search with alpha-beta pruning.

An exercise about minimax, using alpha-beta pruning.

In this section, you'll learn about an intelligent technique that aims to model the approximate mode of reasoning imitating the human ability to make decisions in an environment of uncertainty and imprecision.

In this lesson, let's make a brief introduction about fuzzy logic and learn what is the fuzzification process.

An exercise about fuzzy logic.

In this lesson, will be presented the operation types of fuzzy logic, as well as the linguistic modifiers.

In this lesson, you'll learn how to apply the defuzzification process, changing fuzzy values into real values.

An exercise about defuzzification.

In this section you'll learn about the two types of pathfinding, a technique widely used in games.

In this lesson, you'll learn how to apply artificial intelligence concepts to solve problems involving pathfinding without the use of diagonals.

An exercise about pathfinding (without diagonals).

In this lesson, you'll learn how to apply artificial intelligence concepts to solve problems involving pathfinding with the use of diagonals.

An exercise about pathfinding (with diagonals).

In this section, you'll learn about the artificial neural network and how to solve problems involving perceptron and back propagator networks.

In this lesson, let's start our studies on artificial neural networks and its main characteristics.

In this lesson, let's continue with the study of artificial neural networks, focusing on its topologies and types of learning.

In this lesson, you'll know the components of an artificial neuron, as well as the different functions applied to its activation.

In this lesson, you'll learn about the Perceptron network, and how to perform calculations for its resolution.

An exercise about perceptron.

In this lesson, let's start our studies on the back propagator network.

In this lesson, let's do a practical exercise to solve the first part of back propagator.

In this final lesson, let's proceed with the resolution of the back propagator practicaç exercise.

An exercise about back propagator.

In this section are the resolutions of all the exercises of this course.

Resolution of the first exercise about adjacency arrays.

Resolution of the second exercise about adjacency arrays.

Resolution of the first exercise about breadth-first search.

Resolution of the second exercise about breadth-first search.

Resolution of the exercise about depth-first search.

Resolution of the exercise about best-first.

Resolution of the exercise about A*.

Resolution of the exercise about Kruskal.

Resolution of the first exercise about Dijkstra.

Resolution of the second exercise about Dijkstra.

Resolution of the exercise about prim.

Resolution of the exercise about minimax (2 players).

Resolution of the exercise about minimax (more than 2 players).

Resolution of the exercise about minimax (alpha-beta pruning).

Resolution of the exercise about fuzzy logic.

Resolution of the exercise about defuzzification.

Resolution of the exercise about pathfinding (without diagonals).

Resolution of the exercise about pathfinding (with diagonals).

Resolution of the exercise about perceptron.

Resolution of the exercise about back propagator.

This is a basic course of artificial intelligence. Learn about this much-needed subject in IT without much complication following a brief and explanatory video lessons.

In this online course, we will be discussing various topics within this fascinating subject:

- A brief history of creation and characteristics of artificial intelligence
- Presentation of different problem-solving algorithms
- Logic to transform real-world values and concepts so that a machine can understand
- A brief lessons in the field of artificial neural networks where it's possible to teach a machine how to learn patterns (all with examples solved during classes and exercises to reinforce all learning).

Classes are divided into topics that cover each stage of learning, followed by exercises depending on the subject (exercise resolution is also provided).

At the end of the course, you'll be able to solve several problems that use some of the algorithms or techniques explained during the videos, being able to apply this knowledge both for the digital games area and any other within the branch of information technology.

- You should have basic knowledge of maths and logic.
- You should have the desire to learn new things.

What will be covered throughout the course?

A little video introducing some aspects of the course.

Only a file containing all the references used to make this course.

Some general aspects of Artificial Intelligence.

In this lesson, you'll learn about rational agents, rationality measures, and the PEAS framework.

In this section, you'll start learning the first steps to solve simple AI problems.

In this lesson, let's make a brief introduction about graphs, their properties, and types.

In this lesson, you'll learn about the adjacency arrays.

An exercise about adjacency arrays.

An exercise about adjacency arrays.

An exercise about breadth-first search.

An exercise about breadth-first search.

In this lesson, you'll learn about the depth-first search algorithm.

An exercise about depth-first search.

Heuristics is a cognitive process, used in non-rational decisions. It’s a strategy that ignores part of the information in order to make the choice easier and faster.

In this lesson, will be presented the best-first algorithm for heuristic search.

An exercise about best-first.

In this lesson, will be presented the A* algorithm for heuristic search.

An exercise about A*.

In this section you'll learn algorithms used to perform searches on trees, revealing their minimum weights.

In this lesson, you'll learn how to solve minimal tree searches using the Kruskal algorithm.

An exercise about Kruskal.

In this lesson, you'll learn how to solve minimal tree searches using the Dijkstra algorithm.

An exercise about Dijkstra.

An exercise about Dijkstra.

In this lesson, you'll learn how to solve minimal tree searches using the prim algorithm.

An exercise about prim.

In this section, you'll learn about the three types of minimax searches.

In this lesson, you'll learn how to solve problems involving the minimax search with only 2 players.

An exercise about minimax with 2 players.

An exercise about minimax with more than 2 players.

An exercise about minimax, using alpha-beta pruning.

In this section, you'll learn about an intelligent technique that aims to model the approximate mode of reasoning imitating the human ability to make decisions in an environment of uncertainty and imprecision.

An exercise about fuzzy logic.

An exercise about defuzzification.

In this section you'll learn about the two types of pathfinding, a technique widely used in games.

An exercise about pathfinding (without diagonals).

An exercise about pathfinding (with diagonals).

In this section, you'll learn about the artificial neural network and how to solve problems involving perceptron and back propagator networks.

In this lesson, let's start our studies on artificial neural networks and its main characteristics.

An exercise about perceptron.

In this lesson, let's start our studies on the back propagator network.

In this lesson, let's do a practical exercise to solve the first part of back propagator.

In this final lesson, let's proceed with the resolution of the back propagator practicaç exercise.

An exercise about back propagator.

In this section are the resolutions of all the exercises of this course.

Resolution of the first exercise about adjacency arrays.

Resolution of the second exercise about adjacency arrays.

Resolution of the first exercise about breadth-first search.

Resolution of the second exercise about breadth-first search.

Resolution of the exercise about depth-first search.

Resolution of the exercise about best-first.

Resolution of the exercise about A*.

Resolution of the exercise about Kruskal.

Resolution of the first exercise about Dijkstra.

Resolution of the second exercise about Dijkstra.

Resolution of the exercise about prim.

Resolution of the exercise about minimax (2 players).

Resolution of the exercise about minimax (more than 2 players).

Resolution of the exercise about minimax (alpha-beta pruning).

Resolution of the exercise about fuzzy logic.

Resolution of the exercise about defuzzification.

Resolution of the exercise about pathfinding (without diagonals).

Resolution of the exercise about pathfinding (with diagonals).

Resolution of the exercise about perceptron.

Resolution of the exercise about back propagator.

All Classes

Free for 30 Days

All Classes

Free for 30 Days

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