AI and Meta-Heuristics (Combinatorial Optimization) Python

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

4.47 (130 reviews)
Udemy
platform
English
language
Other
category
instructor
AI and Meta-Heuristics (Combinatorial Optimization) Python
2,171
students
17.5 hours
content
Nov 2023
last update
$84.99
regular price

What you will 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

Why take this course?

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

### 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!

Screenshots

AI and Meta-Heuristics (Combinatorial Optimization) Python - Screenshot_01AI and Meta-Heuristics (Combinatorial Optimization) Python - Screenshot_02AI and Meta-Heuristics (Combinatorial Optimization) Python - Screenshot_03AI and Meta-Heuristics (Combinatorial Optimization) Python - Screenshot_04

Reviews

Thorsten
June 28, 2023
Interesting content that was well-presented. Would have liked more in-depth explanation of applications, pros and cons, convergence properties and similar details for the algorithms instead of spending so much time on going over implementation of code. Also would have appreciated more exercises.
Günter
October 3, 2022
Quite interesting and mostly easy to follow. Sometimes maybe too many repetitions. The last section was a bit harder to follow, maybe because the first real example comes late. Examples are well chosen

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4501302
udemy ID
1/18/2022
course created date
2/26/2022
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