--> AI and Meta-Heuristics (Combinatorial Optimization) Python

# AI and Meta-Heuristics (Combinatorial Optimization) Python

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What you'll learn
• 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

Description
This 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.

### PATHFINDING ALGORITHMS ###

Section 1 - Breadth-First Search (BFS)

why to use graph algorithms in AI

Section 2 - Depth-First Search (DFS)

what is depth-first search algorithm

implementation with iteration and with recursion

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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