AI and Meta-Heuristics (Combinatorial Optimization) Python udemy course free download

What you'll learn:

AI and Meta-Heuristics (Combinatorial Optimization) Python

  • Understand why AI is important.
  • The BFS, DFS, and A* search algorithms can help you find your way around.
  • Understand how heuristics and meta-heuristics work and how they can help you.
  • You understand how genetic algorithms work
  • Understand how particle swarm optimization works.
  • You need to know how it works to understand the term “simulated annealing.”

Requirements::

Description:

Section 3 - A* Search Algorithm

### META-HEURISTICS ###

Section 4 - Simulated Annealing

Section 5 - Genetic Algorithms

Section 6 - Particle Swarm Optimization (PSO)

### GAMES AND GAME TREES ###

Section 7 - Game Trees

Section 8 - Minimax Algorithm and Game Engines

Section 9 - Tic Tac Toe with Minimax

### REINFORCEMENT LEARNING ###

### PYTHON PROGRAMMING CRASH COURSE ###

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.

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