Artificial Intelligence & Games in Java

A guide how to create smart applications, AI, genetic algorithms, pruning, heuristics and metaheuristics
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1,012 students enrolled Bestselling in Artificial Intelligence
Instructed by Holczer Balazs IT & Software / Other
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  • Lectures 67
  • Length 7 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 7/2015 English

Course Description

This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example. We may construct algorithms that can have a very very good guess about stocks movement in the market.

In the first chapter we are going to talk about the basic graph algorithms. Several advanced algorithms can be solved with the help of graphs, so as far as I am concerned these algorithms are the first steps.

Second chapter is about local search: finding minimum and maximum or global optimum in the main. These searches are used frequently when we use regression for example and want to find the parameters for the fit. We will consider basic concepts as well as the more advanced algorithms: heuristics and meta-heuristics.

The last topic will be about minimax algorithm and how to use these technique in games such as chess or tic-tac-toe, how to build and construct a game tree, how to analyze these kinds of tree like structures and so on. We will implement the tic-tac-toegame together in the end.

LAST UPDATE OF THE COURSE: 2016 october

What are the requirements?

  • Basic Java (SE)
  • Some basic algorithms ( maximum/minimum finding )
  • Basic math ( functions )

What am I going to get from this course?

  • 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

What is the target audience?

  • This course is meant for students or anyone who interested in programming and have some background in basic Java

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction
Introduction
Preview
01:37
What is AI good for?
04:39
Complexity theory
Article
Section 2: Graph-Search Algorithms
Graph theory introduction
07:23
Breadth-first search introduction
Preview
09:30
Breadt-first search implementation
12:10
Depth-first search introduction
10:21
Depth-first search implementation I - with stack
11:28
Depth-first search implementation II - with recursion
04:17
Enhanced search algorithms introduction
07:22
Iterative deepening depth-first search (IDDFS)
09:45
A* search implementation I
07:11
A* search implementation II
11:19
A* search implementation III
05:15
Section 3: Basic Search / Optimization Algorithms
Brute-force search introduction
04:21
Brute-force search example
09:15
Stochastic search introduction
04:27
Stochastic search example
08:06
Hill climbing introduction
03:30
Hill climbing example
07:35
Section 4: Meta-Heuristic Optimization Methods
Heuristics VS meta-heuristics
07:34
Tabu search introduction
09:47
---------------- Simulated Annealing ------------------
Article
Simulated annealing introduction
10:19
Simulated annealing - function extremum I
03:47
Simulated annealing - function extremum II
10:48
Simulated annealing - function extremum III
04:24
Travelling salesman problem I - city
09:51
Travelling salesman problem II - tour
13:10
Travelling salesman problem III - annealing algorithm
10:17
Travelling salesman problem IV - testing
04:29
----------------- Genetic Algorithms -------------------
Article
Genetic algorithms introduction - basics
04:25
Genetic algorithms introduction - chromosomes
02:26
Genetic algorithms introduction - crossover
03:33
Genetic algorithms introduction - mutation
03:11
Genetic algorithms introduction - the algorithm
03:16
Genetic algorithm implementation I - individual
09:07
Genetic algorithm implementation II - population
05:36
Genetic algorithm implementation III - the algorithm
09:22
Genetic algorithm implementation IV - testing
07:25
Genetic algorithm implementation V - function optimum
10:50
---------------- Swarm Optimization ------------------
Article
Swarm intelligence intoduction
07:01
Partical swarm optimization introduction I - basics
07:39
Partical swarm optimization introduction II - the algorithm
10:19
Particle swarm optimization implementation I - particle
10:25
Particle swarm optimization implementation II - initialize
07:13
Particle swarm optimization implementation III - the algorithm
10:08
Particle swarm optimization implementation IV - testing
04:05
Section 5: Minimax Algorithm - Game Engines
Game trees introduction
04:13
Minimax algorithm introduction - basics
04:15
Minimax algorithm introduction - the algorithm
07:03
Minimax algorithm introduction - relation with tic-tac-toe
04:35
Alpha-beta pruning introduction
09:23
Chess problem
02:11
Section 6: Tic-Tac-Toe Game
About the game
02:20
Cell
04:29
Constants and Player
03:08
Board class I
13:33
Board class II
07:31
Minimax algorithm
07:53
Game class
10:14
Running tic-tac-toe
04:37
Section 7: Source code
Source code
Article
Slides
Article
Coupon codes - get any of my other courses for a discounted price
Article

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Instructor Biography

Holczer Balazs, Software Engineer

Hi!

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.

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