Ai And Meta-Heuristics (Combinatorial Optimization) Python

Graph Algorithms, Genetic Algorithms, Simulated Annealing, Swarm Intelligence, Heuristics, Minimax and Meta-Heuristics

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

Requirements
No programming experience needed. You will learn everything you need to know.

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)what is breadth-first search algorithmwhy to use graph algorithms in AISection 2 - Depth-First Search (DFS)what is depth-first search algorithmimplementation with iteration and with recursiondepth-first search stack memory visualizationmaze escape applicationSection 3 - A* Search Algorithmwhat is A* search algorithmwhat is the difference between Dijkstra's algorithm and A* searchwhat is a heuristicManhattan distance and Euclidean distance### META-HEURISTICS ###Section 4 - Simulated Annealingwhat is simulated annealinghow to find the extremum of functionshow to solve combinatorial optimization problemstravelling salesman problem (TSP)solving the Sudoku problem with simulated annealingSection 5 - Genetic Algorithmswhat are genetic algorithmsartificial evolution and natural selectioncrossover and mutationsolving the knapsack problem and N queens problemSection 6 - Particle Swarm Optimization (PSO)what is swarm intelligencewhat is the Particle Swarm Optimization algorithm### GAMES AND GAME TREES ###Section 7 - Game Treeswhat are game treeshow to construct game treesSection 8 - Minimax Algorithm and Game Engineswhat is the minimax algorithmwhat is the problem with game trees?using the alpha-beta pruning approachchess problemSection 9 - Tic Tac Toe with MinimaxTic Tac Toe game and its implementationusing minimax algorithmusing alpha-beta pruning algorithm### REINFORCEMENT LEARNING ###Markov Decision Processes (MDPs)reinforcement learning fundamentalsvalue iteration and policy iterationexploration vs exploitation problemmulti-armed bandits problemQ learning algorithmlearning tic tac toe with Q learning ### PYTHON PROGRAMMING CRASH COURSE ###Python programming fundamentalsbasic data structures fundamentals of memory managementobject oriented programming (OOP)NumPyIn 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