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[ DevCourseWeb com ] Udemy - Artificial Intelligence I - Meta-Heuristics and Games in Java
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01 - Introduction
001 Introduction.mp4
001 Introduction_en.srt
02 - Why Should You Learn Artificial Intelligence
001 What is AI good for.mp4
001 What is AI good for_en.srt
03 - ### PATHFINDING ALGORITHMS (GRAPHS) ###
001 Why to consider graph algorithms.mp4
001 Why to consider graph algorithms_en.srt
04 - Breadth-First Search (BFS)
001 What is breadth-first search.mp4
001 What is breadth-first search_en.srt
002 Breadth-first search implementation.mp4
002 Breadth-first search implementation_en.srt
003 Applications of breadth-first search.mp4
003 Applications of breadth-first search_en.srt
05 - Depth-First Search (DFS)
001 What is depth-first search.mp4
001 What is depth-first search_en.srt
002 Depth-first search implementation I - with stack.mp4
002 Depth-first search implementation I - with stack_en.srt
003 Depth-first search implementation II - with recursion.mp4
003 Depth-first search implementation II - with recursion_en.srt
004 Depth-first search and stack memory visualization.mp4
004 Depth-first search and stack memory visualization_en.srt
005 Memory comparison of graph traversal algorithms.mp4
005 Memory comparison of graph traversal algorithms_en.srt
006 Applications of depth-first search.mp4
006 Applications of depth-first search_en.srt
06 - Course Challenge #1 - Maze Escape
001 Maze problem introduction.mp4
001 Maze problem introduction_en.srt
002 Course challenge #1 - maze problem.html
003 Maze problem implementation.mp4
003 Maze problem implementation_en.srt
004 Maze problem stack memory visualization.mp4
004 Maze problem stack memory visualization_en.srt
07 - Iterative Deepening Depth-First Search (IDDFS)
001 Enhanced search algorithms introduction (IDDFS).mp4
001 Enhanced search algorithms introduction (IDDFS)_en.srt
002 Iterative deepening depth-first search (IDDFS) implementation.mp4
002 Iterative deepening depth-first search (IDDFS) implementation_en.srt
08 - A Search Algorithm
001 A search introduction.mp4
001 A search introduction_en.srt
002 A search illustration.mp4
002 A search illustration_en.srt
003 A search implementation I.mp4
003 A search implementation I_en.srt
004 A search implementation II.mp4
004 A search implementation II_en.srt
005 A search implementation III.mp4
005 A search implementation III_en.srt
006 Path finding algorithms comparison.mp4
006 Path finding algorithms comparison_en.srt
09 - ### OPTIMIZATION ###
001 Brute-force method.mp4
001 Brute-force method_en.srt
002 Brute-force method implementation.mp4
002 Brute-force method implementation_en.srt
003 Hill climbing method.mp4
003 Hill climbing method_en.srt
004 Hill climbing method implementation.mp4
004 Hill climbing method implementation_en.srt
10 - ### META-HEURISTICS ###
001 Heuristics and meta-heuristics.mp4
001 Heuristics and meta-heuristics_en.srt
11 - Simulated Annealing
001 What is simulated annealing.mp4
001 What is simulated annealing_en.srt
12 - Simulated Annealing Implementation - Continuous Functions
001 Simulated annealing - function extremum I.mp4
001 Simulated annealing - function extremum I_en.srt
002 Simulated annealing - function extremum II.mp4
002 Simulated annealing - function extremum II_en.srt
003 Simulated annealing - function extremum III.mp4
003 Simulated annealing - function extremum III_en.srt
13 - Simulated Annealing Implementation - Combinatorial Optimization
001 What is the travelling salesman problem.mp4
001 What is the travelling salesman problem_en.srt
002 Travelling salesman problem I - city.mp4
002 Travelling salesman problem I - city_en.srt
003 Travelling salesman problem II - tour.mp4
003 Travelling salesman problem II - tour_en.srt
004 Travelling salesman problem III - annealing algorithm.mp4
004 Travelling salesman problem III - annealing algorithm_en.srt
005 Travelling salesman problem IV - testing.mp4
005 Travelling salesman problem IV - testing_en.srt
14 - Genetic Algorithms
001 Genetic algorithms introduction - basics.mp4
001 Genetic algorithms introduction - basics_en.srt
002 Genetic algorithms introduction - chromosomes.mp4
002 Genetic algorithms introduction - chromosomes_en.srt
003 Genetic algorithms introduction - crossover.mp4
003 Genetic algorithms introduction - crossover_en.srt
004 Genetic algorithms introduction - mutation.mp4
004 Genetic algorithms introduction - mutation_en.srt
005 Genetic algorithms introduction - selection.mp4
005 Genetic algorithms introduction - selection_en.srt
006 Genetic algorithms introduction - the algorithm.mp4
006 Genetic algorithms introduction - the algorithm_en.srt
007 What is elitism.mp4
007 What is elitism_en.srt
008 Advantages and limitations of genetic algorithms.mp4
008 Advantages and limitations of genetic algorithms_en.srt
15 - Genetic Algorithms Implementation - Simple Example
001 Genetic algorithm implementation I - individual.mp4
001 Genetic algorithm implementation I - individual_en.srt
002 Genetic algorithm implementation II - population.mp4
002 Genetic algorithm implementation II - population_en.srt
003 Genetic algorithm implementation III - the algorithm.mp4
003 Genetic algorithm implementation III - the algorithm_en.srt
004 Genetic algorithm implementation IV - testing.mp4
004 Genetic algorithm implementation IV - testing_en.srt
005 Genetic algorithm implementation V - function optimum.mp4
005 Genetic algorithm implementation V - function optimum_en.srt
16 - Course Challenge #2 - Knapsack Problem
001 Knapsack problem introduction.mp4
001 Knapsack problem introduction_en.srt
002 Course challenge #2 - knapsack problem.html
003 Knapsack problem with genetic algorithms.mp4
003 Knapsack problem with genetic algorithms_en.srt
17 - Particle Swarm Optimization
001 What is swarm intelligence.mp4
001 What is swarm intelligence_en.srt
002 Particle swarm optimization introduction I - basics.mp4
002 Particle swarm optimization introduction I - basics_en.srt
003 Particle swarm optimization introduction II - the algorithm.mp4
003 Particle swarm optimization introduction II - the algorithm_en.srt
004 Exploration and exploitation trade-off.mp4
004 Exploration and exploitation trade-off_en.srt
18 - Particle Swarm Optimization - Simple Example
001 Particle swarm optimization implementation I - particle.mp4
001 Particle swarm optimization implementation I - particle_en.srt
002 Particle swarm optimization implementation II - initialize.mp4
002 Particle swarm optimization implementation II - initialize_en.srt
003 Particle swarm optimization implementation III - the algorithm.mp4
003 Particle swarm optimization implementation III - the algorithm_en.srt
004 Particle swarm optimization implementation IV - testing.mp4
004 Particle swarm optimization implementation IV - testing_en.srt
19 - ### TWO PLAYER GAMES ###
001 Game trees introduction.mp4
001 Game trees introduction_en.srt
20 - Minimax Algorithm - Game Engines
001 Minimax algorithm introduction - basics.mp4
001 Minimax algorithm introduction - basics_en.srt
002 Minimax algorithm introduction - the algorithm.mp4
002 Minimax algorithm introduction - the algorithm_en.srt
003 Minimax algorithm introduction - relation to tic-tac-toe.mp4
003 Minimax algorithm introduction - relation to tic-tac-toe_en.srt
004 Alpha-beta pruning introduction.mp4
004 Alpha-beta pruning introduction_en.srt
005 Alpha-beta pruning example.mp4
005 Alpha-beta pruning example_en.srt
006 Chess problem.mp4
006 Chess problem_en.srt
21 - Tic-Tac-Toe Game
001 About the game.mp4
001 About the game_en.srt
002 Cell.mp4
002 Cell_en.srt
003 Constants and Player.mp4
003 Constants and Player_en.srt
004 Game implementation I.mp4
004 Game implementation I_en.srt
005 Game implementation II.mp4
005 Game implementation II_en.srt
006 Board implementation I.mp4
006 Board implementation I_en.srt
007 Board implementation II - isWinning().mp4
007 Board implementation II - isWinning()_en.srt
008 Board implementation III.mp4
008 Board implementation III_en.srt
009 Minimax algorithm.mp4
009 Minimax algorithm_en.srt
010 Minimax algorithm revisited.mp4
010 Minimax algorithm revisited_en.srt
011 Running tic-tac-toe.mp4
011 Running tic-tac-toe_en.srt
012 Minimax algorithm stack memory visualization.mp4
012 Minimax algorithm stack memory visualization_en.srt
22 - Algorhyme FREE Algorithms Visualizer App
001 Algorhyme - Algorithms and Data Structures.html
23 - Course Materials (DOWNLOADS)
001 Course materials.html
newer_slides
ai.pptx
old_slides
ai.pptx
astartalgo.pptx
bruteforce.pptx
geneticalgo.pptx
graphtraverse (1).pptx
graphtraverse.pptx
heuristics.pptx
hill.pptx
iddfs.pptx
pruning.pptx
pso.pptx
simulatedannealing.pptx
stochasticsearch.pptx
tabusearch.pptx
source_code
BruteForceSearch
bin
com
udemy
bruteforce
App.class
BruteForce.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
udemy
bruteforce
App.java
BruteForce.java
GeneticAlgorithms
bin
com
globalsoftwaresupport
App.class
Constants.class
GeneticAlgorithm.class
Individual.class
Population.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
globalsoftwaresupport
App.java
Constants.java
GeneticAlgorithm.java
Individual.java
Population.java
GeneticAlgorithmsII
bin
com
balazsholczer
genetic
App.class
Constants.class
GeneticAlgorithms.class
Individual.class
Population.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
balazsholczer
genetic
App.java
Constants.java
GeneticAlgorithms.java
Individual.java
Population.java
GeneticAlgorithmsKnapsack
bin
com
globalsoftwaresupport
App.class
Constants.class
GeneticAlgorithm.class
Individual.class
Population.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
globalsoftwaresupport
App.java
Constants.java
GeneticAlgorithm.java
Individual.java
Population.java
GraphAlgorithms
AStarSearch
bin
com
globalsoftwaresupport
AStarSearch.class
App.class
Edge.class
Node.class
NodeComparator.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
globalsoftwaresupport
AStarSearch.java
App.java
Edge.java
Node.java
NodeComparator.java
BreadthFirstSearch
App.java
BreadthFirstSearch.java
Vertex.java
DepthFirstSearch
App.java
DepthFirstSearch.java
Vertex.java
IterativeDeepeningSearch
Algorithm.java
App.java
Node.java
theory.txt
HillClimbing
bin
com
udemy
hill
App.class
HillClimbing.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
udemy
hill
App.java
HillClimbing.java
MazeSolver
bin
com
globalsoftwaresupport
App.class
MazeSolver.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
DS_Store
globalsoftwaresupport
App.java
MazeSolver.java
PSO
bin
com
balazsholczer
pso
App.class
Constants.class
Particle.class
ParticleSwarm.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
balazsholczer
pso
App.java
Constants.java
Particle.java
ParticleSwarm.java
RandomSearch
bin
com
udemy
random
App.class
StochasticSearch.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
udemy
random
App.java
StochasticSearch.java
SimulatedAnnealing
bin
com
globalsoftwaresupport
App.class
Constants.class
SimulatedAnnealing.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
globalsoftwaresupport
App.java
Constants.java
SimulatedAnnealing.java
SimulatedAnnealingTSP
bin
com
globalsoftwaresupport
App.class
City.class
Constants.class
Repository.class
SimulatedAnnealing.class
SingleTour.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
globalsoftwaresupport
App.java
City.java
Constants.java
Repository.java
SimulatedAnnealing.java
SingleTour.java
TabuSearch
bin
com
balazsholczer
tabu
App.class
Constants.class
CostFunction.class
NeighborSolutionHandler.class
State.class
TabuList.class
TabuSearch.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
com
balazsholczer
tabu
App.java
Constants.java
CostFunction.java
NeighborSolutionHandler.java
State.java
TabuList.java
TabuSearch.java
TicTacToe
bin
App.class
Board.class
Cell.class
Constants.class
Game.class
Player.class
classpath
project
settings
org.eclipse.jdt.core.prefs
src
App.java
Board.java
Cell.java
Constants.java
Game.java
Player.java
Bonus Resources.txt
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