Artificial Intelligence I: Meta-Heuristics and Games in Java

Graph Algorithms, Genetic Algorithms, Simulated Annealing, Swarm Intelligence, Minimax, Heuristics and Meta-Heuristics
4.39 (833 reviews)
Udemy
platform
English
language
Data Science
category
instructor
Artificial Intelligence I: Meta-Heuristics and Games in Java
8 506
students
9 hours
content
Mar 2022
last update
$99.99
regular price

What you will learn

Get a good grasp of artificial intelligence

Understand how AI algorithms work

Understand graph search algorithms - BFS, DFS and A* search

Understand meta-heuristics

Understand genetic algorithms

Understand simulated annealing

Understand swarm intelligence and particle swarm optimization

Understand game trees

Understand minimax algorithm and alpha-beta pruning

Tic Tac Toe game from scratch with minimax algorithm

Course Gallery

Artificial Intelligence I: Meta-Heuristics and Games in Java – Screenshot 1
Screenshot 1Artificial Intelligence I: Meta-Heuristics and Games in Java
Artificial Intelligence I: Meta-Heuristics and Games in Java – Screenshot 2
Screenshot 2Artificial Intelligence I: Meta-Heuristics and Games in Java
Artificial Intelligence I: Meta-Heuristics and Games in Java – Screenshot 3
Screenshot 3Artificial Intelligence I: Meta-Heuristics and Games in Java
Artificial Intelligence I: Meta-Heuristics and Games in Java – Screenshot 4
Screenshot 4Artificial Intelligence I: Meta-Heuristics and Games in Java

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Comidoc Review

Our Verdict

As an introductory course on AI algorithms in Java, this Udemy offering by István Hoczer provides a comprehensive overview and solid foundation. Students will appreciate the combination of theoretical explanations and practical demonstrations, yet may find areas where greater detail or updated coding examples could further enhance their learning experience.

What We Liked

  • In-depth understanding of graph search algorithms, genetic algorithms, simulated annealing, swarm intelligence, and minimax algorithm
  • Practical Java implementations for each concept, helping students grasp the underlying code structure easily
  • Well-structured course with clear explanations, making even complex topics easier to understand
  • Up-to-date in 2022, ensuring that the information remains relevant in today's AI landscape

Potential Drawbacks

  • Some topics could benefit from more detailed and clearer presentations
  • Minimal structured exercises for practicing the concepts taught
  • Older-style Java coding examples, which may not align with contemporary conventions
528460
udemy ID
15/06/2015
course created date
05/08/2019
course indexed date
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