AI and Meta-Heuristics (Combinatorial Optimization) Python

What you'll learn:

AI and Meta-Heuristics (Combinatorial Optimization) Python

Requirements:

• Programming skills are not required. Everything you need to know will be learned.

Description:

• depth-first search stack memory visualization

• maze escape application

Section 3 - A* Search Algorithm

• what is A* search algorithm

• what is the difference between Dijkstra's algorithm and A* search

• what is a heuristic

• Manhattan distance and Euclidean distance

### META-HEURISTICS ###

Section 4 - Simulated Annealing

• what is simulated annealing

• how to find the extremum of functions

• how to solve combinatorial optimization problems

• travelling salesman problem (TSP)

• solving the Sudoku problem with simulated annealing

Section 5 - Genetic Algorithms

• what are genetic algorithms

• artificial evolution and natural selection

• crossover and mutation

• solving the knapsack problem and N queens problem

Section 6 - Particle Swarm Optimization (PSO)

• what is swarm intelligence

• what is the Particle Swarm Optimization algorithm

### GAMES AND GAME TREES ###

Section 7 - Game Trees

• what are game trees

• how to construct game trees

Section 8 - Minimax Algorithm and Game Engines

• what is the minimax algorithm

• what is the problem with game trees?

• using the alpha-beta pruning approach

• chess problem

Section 9 - Tic Tac Toe with Minimax

• Tic Tac Toe game and its implementation

• using minimax algorithm

• using alpha-beta pruning algorithm

### REINFORCEMENT LEARNING ###

• Markov Decision Processes (MDPs)

• reinforcement learning fundamentals

• value iteration and policy iteration

• exploration vs exploitation problem

• multi-armed bandits problem

• Q learning algorithm

• learning tic tac toe with Q learning

### PYTHON PROGRAMMING CRASH COURSE ###

• Python programming fundamentals

• basic data structures

• fundamentals of memory management

• object oriented programming (OOP)

• NumPy

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.

Thanks for joining the course, let's get started!

Who this course is for:

• Beginner Python programmers curious about artificial intelligence and combinatorial optimization
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Course Details:

• 17.5 hours on-demand video
• 32 articles
• 18 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of completion

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

People use graph algorithms, Genetic Algorithms, Simulated Annealing and Swarm Intelligence. They also use heuristics, minimaxes, and meta-heuristics.