AI and Meta-Heuristics (Combinatorial Optimization) Python
AI and Meta-Heuristics (Combinatorial Optimization) Python
Graph Algorithms, Genetic Algorithms, Simulated Annealing, Swarm Intelligence, Heuristics, Minimax and Meta-Heuristics
Created by Holczer Balazs | 17.5 hours on-demand video course
This Genetic Algorithms course is about the fundamental concepts of artificial intelligence and meta-heuristics with Python. 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.
In the first chapters we are going to talk about the fundamental graph algorithms – breadth-first search (BFS), depth-first search (DFS) and A* search algorithms. Several advanced algorithms can be solved with the help of graphs, so in my opinion these algorithms are crucial.
The next chapters are about heuristics and meta-heuristics. We will consider the theory as well as the implementation of simulated annealing, genetic algorithms and particle swarm optimization – with several problems such as the famous N queens problem, travelling salesman problem (TSP) etc.
What you’ll learn in Genetic Algorithms Course
- understand why artificial intelligence is important
- understand pathfinding algorithms (BFS, DFS and A* search)
- understand heuristics and meta-heuristics
- understand genetic algorithms
- understand particle swarm optimization
- understand simulated annealing
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Who this Genetic Algorithms course is for:
- Beginner Python programmers curious about artificial intelligence and combinatorial optimization
