AI and Meta-Heuristics (Combinatorial Optimization) Python

AI and Meta-Heuristics (Combinatorial Optimization) Python, available at $84.99, has an average rating of 4.46, with 199 lectures, 12 quizzes, based on 149 reviews, and has 2433 subscribers.

You will learn about 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 ideal for individuals who are Beginner Python programmers curious about artificial intelligence and combinatorial optimization It is particularly useful for Beginner Python programmers curious about artificial intelligence and combinatorial optimization.

Enroll now: AI and Meta-Heuristics (Combinatorial Optimization) Python

Summary

Title: AI and Meta-Heuristics (Combinatorial Optimization) Python

Price: $84.99

Average Rating: 4.46

Number of Lectures: 199

Number of Quizzes: 12

Number of Published Lectures: 196

Number of Published Quizzes: 12

Number of Curriculum Items: 211

Number of Published Curriculum Objects: 208

Original Price: $19.99

Quality Status: approved

Status: Live

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

Who Should Attend

Beginner Python programmers curious about artificial intelligence and combinatorial optimization

Target Audiences

Beginner Python programmers curious about artificial intelligence and combinatorial optimization

This course is about the fundamental concepts of artificial intelligenceand 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 algorithmsand 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Slides and source code

Chapter 2: ### GRAPH ALGORITHMS ###

Lecture 1: Why to consider graph algorithms?

Chapter 3: Breadth-First Search (DFS) Algorithm

Lecture 1: What is breadth-first search?

Lecture 2: Breadth-first search implementation

Lecture 3: Applications of breadth-first search

Chapter 4: Challange #1 – WebCrawler

Lecture 1: Course challenge #1 – WebCrawler problem

Lecture 2: What are WebCrawlers (core of search engines)?

Lecture 3: WebCrawler basic implementation

Chapter 5: Depth-First Search (DFS) Algorithm

Lecture 1: What is depth-first search?

Lecture 2: Depth-first search implementation

Lecture 3: Depth-first search implementation with recursion

Lecture 4: Depth-first search and stack memory visualization

Lecture 5: Memory comparison of graph traversal algorithms

Lecture 6: Applications of depth-first search

Chapter 6: Challange #2 – Maze Solver

Lecture 1: Course challenge #2 – maze problem

Lecture 2: Maze problem introduction

Lecture 3: Maze problem implementation

Lecture 4: Maze problem stack memory visualization

Chapter 7: A* Search Algorithm

Lecture 1: What is the A* search algorithm?

Lecture 2: A* search illustration

Lecture 3: A* search implementation I

Lecture 4: A* search implementation II

Lecture 5: A* search implementation III

Lecture 6: Path finding algorithms comparison

Chapter 8: ### META-HEURISTICS ###

Lecture 1: What are meta-heuristic approaches?

Chapter 9: Simulated Annealing

Lecture 1: What is simulated annealing?

Chapter 10: Simulated Annealing Implementation – Continuous Functions

Lecture 1: Simulated annealing implementation I

Lecture 2: Simulated annealing implementation II

Lecture 3: Simulated annealing implementation III

Chapter 11: Simulated Annealing Implementation – Combinatorial Optimization

Lecture 1: What is the travelling salesman problem (TSP)?

Lecture 2: Travelling salesman problem implementation I

Lecture 3: Travelling salesman problem implementation II

Lecture 4: Travelling salesman problem implementation III

Lecture 5: Travelling salesman problem implementation IV

Chapter 12: Simulated Annealing Implementation – Sudoku

Lecture 1: What is the Sudoku problem?

Lecture 2: Sudoku problem implementation I

Lecture 3: Sudoku problem implementation II

Lecture 4: Sudoku problem implementation III

Lecture 5: Sudoku problem implementation IV

Chapter 13: Genetic Algorithms

Lecture 1: Genetic algorithms introduction – basics

Lecture 2: Genetic algorithms introduction – chromosomes

Lecture 3: Genetic algorithms introduction – crossover

Lecture 4: Genetic algorithms introduction – mutation

Lecture 5: Genetic algorithms introduction – selection

Lecture 6: Genetic algorithms introduction – the algorithm

Lecture 7: What is elitism?

Lecture 8: Advantages and limitations of genetic algorithms

Chapter 14: Genetic Algorithms Implementation – Simple Example

Lecture 1: Genetic algorithm implementation I

Lecture 2: Genetic algorithm implementation II

Lecture 3: Genetic algorithm implementation III

Lecture 4: Genetic algorithm implementation IV

Lecture 5: Genetic algorithm implementation V – elitism

Chapter 15: Genetic Algorithms Implementation – Constraint Satisfaction Problems

Lecture 1: What is the N-queens problem?

Lecture 2: N queens problem implementation I

Lecture 3: N queens problem implementation II

Chapter 16: Challenge #3 – Knapsack Problem

Lecture 1: Course challenge #3 – knapsack problem overview

Lecture 2: What is the knapsack problem?

Lecture 3: Knapsack problem implementation

Chapter 17: Particle Swarm Optimization

Lecture 1: What is swarm intelligence?

Lecture 2: Particle swarm optimization introduction – basics

Lecture 3: Particle swarm optimization introduction – the algorithm

Lecture 4: Exploration and exploitation trade-off

Chapter 18: Particle Swarm Optimization – Simple Example

Lecture 1: Particle swarm optimization implementation I

Lecture 2: Particle swarm optimization implementation II

Lecture 3: Particle swarm optimization implementation III

Chapter 19: ### TWO PLAYER GAMES ###

Lecture 1: Game trees introduction

Chapter 20: Minimax Algorithm – Game Engines

Lecture 1: Minimax algorithm introduction – basics

Lecture 2: Minimax algorithm introduction – the algorithm

Lecture 3: Minimax algorithm introduction – relation to tic-tac-toe

Lecture 4: Alpha-beta pruning introduction

Lecture 5: Alpha-beta pruning example

Instructors

Holczer Balazs
Software Engineer

Rating Distribution

1 stars: 1 votes

2 stars: 3 votes

3 stars: 10 votes

4 stars: 45 votes

5 stars: 90 votes

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