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What you would learn in AI and Meta-Heuristics (Combinatorial Optimization) Python course?
This course focuses on the basic concepts that underlie artificial Intelligenceand meta-heuristics using Python. This subject is becoming extremely hot because these algorithms can be applied to many fields, including software engineering and investment banking. For instance, learning algorithms can detect patterns that can aid in detecting cancer. It is possible to develop algorithms that can make a highly accurate estimate of price movements on the market.
# PATHFINDING ALGORITHMS ##
Section 1. Breadth First Search (BFS)
What is a breadth-first-first-search algorithm?
the reason graph algorithms should be used to aid in AI
Section 2 Depth First Search (DFS)
What is the depth-first search algorithm?
Implementation with iteration and the use of recursion
Memory visualization of the depth-first search stack
maze escape application
Section 3 The A* Algorithm for Search
What exactly is an A*-search algorithm?
What is the main difference between the Dijkstra algorithm and the A* search?
What is a theoretical
Manhattan distance and Euclidean distance
### META-HEURISTICS ###
Section 4- Simulated Annealing
What is simulating annealing?
How do you find the extreme functions
how do you solve combinatorial optimization issues
Traveling salesman issue (TSP)
Solving the Sudoku problem by the simulated the Sudoku problem using annealing
Section 5 Genetic Algorithms
What are genetic algorithms?
Natural selection and artificial evolution
Mutation and crossover
solving the knapsack puzzle and N queens problems
Section 6 - Particle Swarm Optimization (PSO)
What exactly is swarm intelligence?
What is the Particle Swarm? Optimization algorithm?
#GAMES AND GAMES TREE #"##
Section 7 Game Trees
What are game trees?
How to build game trees
Section 8 Minimax The Algorithm as well as Game Engines
What is the minimax algorithm?
What is the issue in-game tree games?
By using the alpha-beta pruning approach.
Section 9 Tic Tac Toe using Minimax
Tic Tac Toe game and its application
employing the alpha-beta pruning algorithm.
#REINFORCEMENT LEARNING # ##
Markov Decision Processes (MDPs)
Fundamentals of reinforcement learning
Value iteration and policy iteration
exploration vs. exploitation problem
Multi-armed bandits are an issue
Q Learning algorithm
Tic tac toe learning with Q-learning
### Python PROGRAMMING CASH COURSE ###
Python programming fundamentals
fundamental data structures
The fundamentals of managing memory
object-oriented programming (OOP)
In the initial chapters, we will examine the essential graph algorithm that includes breadth-first-search (BFS), deep-first search (DFS), and the A* algorithms for search. Graphs can solve many sophisticated algorithms, and I believe these algorithms are vital.
The following chapters will focus on meta-heuristics and heuristics. We will look at the theory and the application of the simulated annealing process and Genetic algorithms and particle optimization for swarms with various problems like the well-known N queens problem, the traveling salesman's problem (TSP), and many more.
Comprehend why artificial intelligence is so important.
Learn about algorithmic pathfinding (BFS, DFS, and A* search)
learn about meta-heuristics as well as heuristics.
Learn to understand the genetic algorithms
Understand the importance of particle Swarm Optimization
Learn about the concept of simulated an annealing
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