AI and Meta Heuristics Combinatorial Optimization Python

Last Updated 08/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 196 lectures (17h 32m) | Size: 5.3 GB

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

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.


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


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


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


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 fundamentals

basic data structures

fundamentals of memory management

object oriented programming (OOP)


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

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






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