**Artificial Intelligence I: Basics and Games in Java**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 6.5 Hours | 1.04 GB

A guide how to create smart applications, AI, genetic algorithms, pruning, heuristics and metaheuristics

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 detecting cancer for example. We may construct algorithms that can have a very very good guess about stock price 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 this 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-toe game together in the end.

What You Will 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

**Table of Contents**

**Introduction**

1 Introduction

2 What is AI good for?

3 Complexity theory

**Graph-Search Algorithms**

4 Why to consider graph algorithms?

5 Breadth-first search introduction

6 Breadt-first search implementation

7 Depth-first search introduction

8 Depth-first search implementation I - with stack

9 Depth-first search implementation II - with recursion

10 Enhanced search algorithms introduction

11 Iterative deepening depth-first search (IDDFS)

12 A* search introduction

**Basic Search / Optimization Algorithms**

13 Brute-force search introduction

14 Brute-force search example

15 Stochastic search introduction

16 Stochastic search example

17 Hill climbing introduction

18 Hill climbing example

**Meta-Heuristic Optimization Methods**

19 Heuristics VS meta-heuristics

20 Tabu search introduction

21 SIMULATED ANNEALING

22 Simulated annealing introduction

23 Simulated annealing - function extremum I

24 Simulated annealing - function extremum II

25 Simulated annealing - function extremum III

26 Travelling salesman problem I - city

27 Travelling salesman problem II - tour

28 Travelling salesman problem III - annealing algorithm

29 Travelling salesman problem IV - testing

30 GENETIC ALGORITHMS

31 Genetic algorithms introduction - basics

32 Genetic algorithms introduction - chromosomes

33 Genetic algorithms introduction - crossover

34 Genetic algorithms introduction - mutation

35 Genetic algorithms introduction - the algorithm

36 Genetic algorithm implementation I - individual

37 Genetic algorithm implementation II - population

38 Genetic algorithm implementation III - the algorithm

39 Genetic algorithm implementation IV - testing

40 Genetic algorithm implementation V - function optimum

41 SWARM OPTIMIZATION

42 Swarm intelligence intoduction

43 Partical swarm optimization introduction I - basics

44 Partical swarm optimization introduction II - the algorithm

45 Particle swarm optimization implementation I - particle

46 Particle swarm optimization implementation II - initialize

47 Particle swarm optimization implementation III - the algorithm

48 Particle swarm optimization implementation IV - testing

**Minimax Algorithm - Game Engines**

49 Game trees introduction

50 Minimax algorithm introduction - basics

51 Minimax algorithm introduction - the algorithm

52 Minimax algorithm introduction - relation with tic-tac-toe

53 Alpha-beta pruning introduction

54 Alpha-beta pruning example

55 Chess problem

**Tic-Tac-Toe Game**

56 About the game

57 Cell

58 Constants and Player

59 Game implementation I

60 Game implementation II

61 Board implementation I

62 Board implementationj II - isWinning()

63 Board implementation III

64 Minimax algorithm

65 Running tic-tac-toe

**Source code**

66 Source code

67 Slides

68 Coupon codes - get any of my other courses for a discounted price

Resolve the captcha to access the links!