Udemy - Artificial Intelligence I: Basics and Games in Java

Category: Study


Posted on 2019-08-19, by phaelx.

Description



Date: March 2019
Author: Holczer Balazs

Size: 1.2 GB
Format: MP4
Download     >>    https://usersdrive.com/kuyla3qjeef9.html
What you'll learn
   *Get a good grasp of artificial intelligence
   *Understand how AI algorithms work
   *Able to create AI algorithms on your own from scratch
   *Understand meta-heuristics


Course content

Introduction
   Introduction
   What is AI good for?

Graph-Search Algorithms (Path Finding Algorithms)
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison

Basic Search / Optimization Algorithms
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison
   Brute-force search introduction
   Brute-force search example
   Stochastic search introduction
   Stochastic search example
   Hill climbing introduction
   Hill climbing example

Meta-Heuristic Optimization Methods
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison
   Brute-force search introduction
   Brute-force search example
   Stochastic search introduction
   Stochastic search example
   Hill climbing introduction
   Hill climbing example
   Heuristics VS meta-heuristics

Tabu Search
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison
   Brute-force search introduction
   Brute-force search example
   Stochastic search introduction
   Stochastic search example
   Hill climbing introduction
   Hill climbing example
   Heuristics VS meta-heuristics
   Tabu search introduction - basics
   Tabu search introduction - tabu tenure
   Tabu search illustration
   Tabu search implementation I
   Tabu search implementation II

Simulated Annealing
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison
   Brute-force search introduction
   Brute-force search example
   Stochastic search introduction
   Stochastic search example
   Hill climbing introduction
   Hill climbing example
   Heuristics VS meta-heuristics
   Tabu search introduction - basics
   Tabu search introduction - tabu tenure
   Tabu search illustration
   Tabu search implementation I
   Tabu search implementation II
   Simulated annealing introduction
   Simulated annealing - function extremum I
   Simulated annealing - function extremum II
   Simulated annealing - function extremum III
   Travelling salesman problem I - city
   Travelling salesman problem II - tour
   Travelling salesman problem III - annealing algorithm
   Travelling salesman problem IV - testing

Genetic Algorithms
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison
   Brute-force search introduction
   Brute-force search example
   Stochastic search introduction
   Stochastic search example
   Hill climbing introduction
   Hill climbing example
   Heuristics VS meta-heuristics
   Tabu search introduction - basics
   Tabu search introduction - tabu tenure
   Tabu search illustration
   Tabu search implementation I
   Tabu search implementation II
   Simulated annealing introduction
   Simulated annealing - function extremum I
   Simulated annealing - function extremum II
   Simulated annealing - function extremum III
   Travelling salesman problem I - city
   Travelling salesman problem II - tour
   Travelling salesman problem III - annealing algorithm
   Travelling salesman problem IV - testing
   Genetic algorithms introduction - basics
   Genetic algorithms introduction - chromosomes
   Genetic algorithms introduction - crossover
   Genetic algorithms introduction - mutation
   Genetic algorithms introduction - the algorithm
   Genetic algorithm implementation I - individual
   Genetic algorithm implementation II - population
   Genetic algorithm implementation III - the algorithm
   Genetic algorithm implementation IV - testing
   Genetic algorithm implementation V - function optimum

Particle Swarm Optimization
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison
   Brute-force search introduction
   Brute-force search example
   Stochastic search introduction
   Stochastic search example
   Hill climbing introduction
   Hill climbing example
   Heuristics VS meta-heuristics
   Tabu search introduction - basics
   Tabu search introduction - tabu tenure
   Tabu search illustration
   Tabu search implementation I
   Tabu search implementation II
   Simulated annealing introduction
   Simulated annealing - function extremum I
   Simulated annealing - function extremum II
   Simulated annealing - function extremum III
   Travelling salesman problem I - city
   Travelling salesman problem II - tour
   Travelling salesman problem III - annealing algorithm
   Travelling salesman problem IV - testing
   Genetic algorithms introduction - basics
   Genetic algorithms introduction - chromosomes
   Genetic algorithms introduction - crossover
   Genetic algorithms introduction - mutation
   Genetic algorithms introduction - the algorithm
   Genetic algorithm implementation I - individual
   Genetic algorithm implementation II - population
   Genetic algorithm implementation III - the algorithm
   Genetic algorithm implementation IV - testing
   Genetic algorithm implementation V - function optimum
   Swarm intelligence intoduction
   Particle swarm optimization introduction I - basics
   Particle swarm optimization introduction II - the algorithm
   Particle swarm optimization implementation I - particle
   Particle swarm optimization implementation II - initialize
   Particle swarm optimization implementation III - the algorithm
   Particle swarm optimization implementation IV - testing

Minimax Algorithm - Game Engines
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison
   Brute-force search introduction
   Brute-force search example
   Stochastic search introduction
   Stochastic search example
   Hill climbing introduction
   Hill climbing example
   Heuristics VS meta-heuristics
   Tabu search introduction - basics
   Tabu search introduction - tabu tenure
   Tabu search illustration
   Tabu search implementation I
   Tabu search implementation II
   Simulated annealing introduction
   Simulated annealing - function extremum I
   Simulated annealing - function extremum II
   Simulated annealing - function extremum III
   Travelling salesman problem I - city
   Travelling salesman problem II - tour
   Travelling salesman problem III - annealing algorithm
   Travelling salesman problem IV - testing
   Genetic algorithms introduction - basics
   Genetic algorithms introduction - chromosomes
   Genetic algorithms introduction - crossover
   Genetic algorithms introduction - mutation
   Genetic algorithms introduction - the algorithm
   Genetic algorithm implementation I - individual
   Genetic algorithm implementation II - population
   Genetic algorithm implementation III - the algorithm
   Genetic algorithm implementation IV - testing
   Genetic algorithm implementation V - function optimum
   Swarm intelligence intoduction
   Particle swarm optimization introduction I - basics
   Particle swarm optimization introduction II - the algorithm
   Particle swarm optimization implementation I - particle
   Particle swarm optimization implementation II - initialize
   Particle swarm optimization implementation III - the algorithm
   Particle swarm optimization implementation IV - testing
   Game trees introduction
   Minimax algorithm introduction - basics
   Minimax algorithm introduction - the algorithm
   Minimax algorithm introduction - relation with tic-tac-toe
   Alpha-beta pruning introduction
   Alpha-beta pruning example
   Chess problem

Tic-Tac-Toe Game
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison
   Brute-force search introduction
   Brute-force search example
   Stochastic search introduction
   Stochastic search example
   Hill climbing introduction
   Hill climbing example
   Heuristics VS meta-heuristics
   Tabu search introduction - basics
   Tabu search introduction - tabu tenure
   Tabu search illustration
   Tabu search implementation I
   Tabu search implementation II
   Simulated annealing introduction
   Simulated annealing - function extremum I
   Simulated annealing - function extremum II
   Simulated annealing - function extremum III
   Travelling salesman problem I - city
   Travelling salesman problem II - tour
   Travelling salesman problem III - annealing algorithm
   Travelling salesman problem IV - testing
   Genetic algorithms introduction - basics
   Genetic algorithms introduction - chromosomes
   Genetic algorithms introduction - crossover
   Genetic algorithms introduction - mutation
   Genetic algorithms introduction - the algorithm
   Genetic algorithm implementation I - individual
   Genetic algorithm implementation II - population
   Genetic algorithm implementation III - the algorithm
   Genetic algorithm implementation IV - testing
   Genetic algorithm implementation V - function optimum
   Swarm intelligence intoduction
   Particle swarm optimization introduction I - basics
   Particle swarm optimization introduction II - the algorithm
   Particle swarm optimization implementation I - particle
   Particle swarm optimization implementation II - initialize
   Particle swarm optimization implementation III - the algorithm
   Particle swarm optimization implementation IV - testing
   Game trees introduction
   Minimax algorithm introduction - basics
   Minimax algorithm introduction - the algorithm
   Minimax algorithm introduction - relation with tic-tac-toe
   Alpha-beta pruning introduction
   Alpha-beta pruning example
   Chess problem
   About the game
   Cell
   Constants and Player
   Game implementation I
   Game implementation II
   Board implementation I
   Board implementationj II - isWinning()
   Board implementation III
   Minimax algorithm
   Running tic-tac-toe

Course Materials (DOWNLOADS)
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison
   Brute-force search introduction
   Brute-force search example
   Stochastic search introduction
   Stochastic search example
   Hill climbing introduction
   Hill climbing example
   Heuristics VS meta-heuristics
   Tabu search introduction - basics
   Tabu search introduction - tabu tenure
   Tabu search illustration
   Tabu search implementation I
   Tabu search implementation II
   Simulated annealing introduction
   Simulated annealing - function extremum I
   Simulated annealing - function extremum II
   Simulated annealing - function extremum III
   Travelling salesman problem I - city
   Travelling salesman problem II - tour
   Travelling salesman problem III - annealing algorithm
   Travelling salesman problem IV - testing
   Genetic algorithms introduction - basics
   Genetic algorithms introduction - chromosomes
   Genetic algorithms introduction - crossover
   Genetic algorithms introduction - mutation
   Genetic algorithms introduction - the algorithm
   Genetic algorithm implementation I - individual
   Genetic algorithm implementation II - population
   Genetic algorithm implementation III - the algorithm
   Genetic algorithm implementation IV - testing
   Genetic algorithm implementation V - function optimum
   Swarm intelligence intoduction
   Particle swarm optimization introduction I - basics
   Particle swarm optimization introduction II - the algorithm
   Particle swarm optimization implementation I - particle
   Particle swarm optimization implementation II - initialize
   Particle swarm optimization implementation III - the algorithm
   Particle swarm optimization implementation IV - testing
   Game trees introduction
   Minimax algorithm introduction - basics
   Minimax algorithm introduction - the algorithm
   Minimax algorithm introduction - relation with tic-tac-toe
   Alpha-beta pruning introduction
   Alpha-beta pruning example
   Chess problem
   About the game
   Cell
   Constants and Player
   Game implementation I
   Game implementation II
   Board implementation I
   Board implementationj II - isWinning()
   Board implementation III
   Minimax algorithm
   Running tic-tac-toe
   Course materials

DISCOUNT FOR OTHER COURSES!
   Introduction
   What is AI good for?
   Why to consider graph algorithms?
   Breadth-first search introduction
   Breadt-first search implementation
   Depth-first search introduction
   Depth-first search implementation I - with stack
   Depth-first search implementation II - with recursion
   Enhanced search algorithms introduction
   Iterative deepening depth-first search (IDDFS)
   A* search introduction
   A* search illustration
   A* search implementation I
   A* search implementation II
   Path finding algorithms comparison
   Brute-force search introduction
   Brute-force search example
   Stochastic search introduction
   Stochastic search example
   Hill climbing introduction
   Hill climbing example
   Heuristics VS meta-heuristics
   Tabu search introduction - basics
   Tabu search introduction - tabu tenure
   Tabu search illustration
   Tabu search implementation I
   Tabu search implementation II
   Simulated annealing introduction
   Simulated annealing - function extremum I
   Simulated annealing - function extremum II
   Simulated annealing - function extremum III
   Travelling salesman problem I - city
   Travelling salesman problem II - tour
   Travelling salesman problem III - annealing algorithm
   Travelling salesman problem IV - testing
   Genetic algorithms introduction - basics
   Genetic algorithms introduction - chromosomes
   Genetic algorithms introduction - crossover
   Genetic algorithms introduction - mutation
   Genetic algorithms introduction - the algorithm
   Genetic algorithm implementation I - individual
   Genetic algorithm implementation II - population
   Genetic algorithm implementation III - the algorithm
   Genetic algorithm implementation IV - testing
   Genetic algorithm implementation V - function optimum
   Swarm intelligence intoduction
   Particle swarm optimization introduction I - basics
   Particle swarm optimization introduction II - the algorithm
   Particle swarm optimization implementation I - particle
   Particle swarm optimization implementation II - initialize
   Particle swarm optimization implementation III - the algorithm
   Particle swarm optimization implementation IV - testing
   Game trees introduction
   Minimax algorithm introduction - basics
   Minimax algorithm introduction - the algorithm
   Minimax algorithm introduction - relation with tic-tac-toe
   Alpha-beta pruning introduction
   Alpha-beta pruning example
   Chess problem
   About the game
   Cell
   Constants and Player
   Game implementation I
   Game implementation II
   Board implementation I
   Board implementationj II - isWinning()
   Board implementation III
   Minimax algorithm
   Running tic-tac-toe
   Course materials
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