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

A guide how to create smart applications, AI, genetic algorithms, pruning, heuristics and metaheuristics
Bestselling
4.4 (97 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,472 students enrolled
Created by Holczer Balazs
Last updated 2/2017
English
Current price: $10 Original price: $50 Discount: 80% off
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Includes:
  • 6.5 hours on-demand video
  • 7 Articles
  • 4 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I 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
View Curriculum
Requirements
  • Basic Java (SE)
  • Some basic algorithms ( maximum/minimum finding )
  • Basic math ( functions )
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

Who is the target audience?
  • This course is meant for students or anyone who interested in programming and have some background in basic Java
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Curriculum For This Course
Expand All 64 Lectures Collapse All 64 Lectures 06:38:34
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Introduction
3 Lectures 06:21

What is AI good for?
04:39

Complexity theory
00:05
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Graph-Search Algorithms
8 Lectures 01:08:56

Breadt-first search implementation
12:10

Depth-first search introduction
10:21

Depth-first search implementation I - with stack
11:23

Depth-first search implementation II - with recursion
04:17

Enhanced search algorithms introduction
03:57

Iterative deepening depth-first search (IDDFS)
10:10

A* search introduction
07:08
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Basic Search / Optimization Algorithms
6 Lectures 37:14
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
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Meta-Heuristic Optimization Methods
30 Lectures 03:20:28
Heuristics VS meta-heuristics
07:34

Tabu search introduction
09:47

SIMULATED ANNEALING
00:00

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
00:00

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
00:00

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
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Minimax Algorithm - Game Engines
6 Lectures 31:40
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
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Tic-Tac-Toe Game
8 Lectures 53:45
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
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Source code
3 Lectures 00:09
Source code
00:01

Slides
00:01

Coupon codes - get any of my other courses for a discounted price
00:06
About the Instructor
Holczer Balazs
4.4 Average rating
2,182 Reviews
22,716 Students
19 Courses
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.

Take a look at my website and join my email list if you are interested in these topics!