Artificial Intelligence I: Basics And Games In Java by Holczer Balazs
Artificial Intelligence I: Basics And Games In Java by Holczer Balazs
Guide how to create smart applications, AI, genetic algorithms, pruning, heuristics and metaheuristics
What you’ll learn
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 good guess about stock price movement in the market.
Section 1:
path findinf algorithms
graph traversal (BFS and DFS)
enhanced search algorihtms
A* search algorithm
Section 2:
basic optimization algorithms
brute-force search
stochastic search and hill climbing algorithm
Section 3:
heuristics and meta-heuristics
tabu search
simulated annealing
genetic algorithms
particle swarm optimization
Section 4:
minimax algorithm
game trees
applications of game trees in chess
tic tac toe game and its implementation
Last updated 4/2019
English
Size: 1.24 GB
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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
- Basic Java (SE)
- Some basic algorithms ( maximum/minimum finding )
- Basic math ( functions )
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 good guess about stock price movement in the market.
Section 1:
path findinf algorithms
graph traversal (BFS and DFS)
enhanced search algorihtms
A* search algorithm
Section 2:
basic optimization algorithms
brute-force search
stochastic search and hill climbing algorithm
Section 3:
heuristics and meta-heuristics
tabu search
simulated annealing
genetic algorithms
particle swarm optimization
Section 4:
minimax algorithm
game trees
applications of game trees in chess
tic tac toe game and its implementation
- 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.
- This course is meant for students or anyone who interested in programming and have some background in basic Java
Last updated 4/2019
English
Size: 1.24 GB
DOWNLOAD -
