A.I. and Combinatorial Optimization with Meta-Heuristics

Graph Algorithms, Genetic Algorithms, Simulated Annealing, Swarm Intelligence, Heuristics 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

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 algorithm

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

### 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