AI and Meta-Heuristics (Combinatorial Optimization) Python

سرفصل های دوره

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

1. Introduction

1. Introduction

2.1 meta_heuristics_ai.zip

2. Slides and source code.html

2. ### GRAPH ALGORITHMS ###

1. Why to consider graph algorithms

3. Breadth-First Search (DFS) Algorithm

1. What is breadth-first search

2.1 breadthfirstsearch.zip

2. Breadth-first search implementation

3. Applications of breadth-first search

4. Breadth-First Search Quiz.html

4. Challange #1 - WebCrawler

1. Course challenge #1 - WebCrawler problem.html

2. What are WebCrawlers (core of search engines)

3.1 webcrawler.zip

3. WebCrawler basic implementation

5. Depth-First Search (DFS) Algorithm

1. What is depth-first search

2.1 depthfirstsearch.zip

2. Depth-first search implementation

3.1 depthfirstsearchrecursion.zip

3. Depth-first search implementation with recursion

4. Depth-first search and stack memory visualization

5. Memory comparison of graph traversal algorithms

6. Applications of depth-first search

7. Depth-First Search Quiz.html

6. Challange #2 - Maze Solver

1. Course challenge #2 - maze problem.html

2. Maze problem introduction

3.1 mazeproblem.zip

3. Maze problem implementation

4. Maze problem stack memory visualization

7. A Search Algorithm

1. What is the A search algorithm

2. A search illustration

3. A search implementation I

4. A search implementation II

5.1 astarsearch.zip

5. A search implementation III

6. Path finding algorithms comparison

7. A Search Quiz.html

8. ### META-HEURISTICS ###

1. What are meta-heuristic approaches

2. Heuristics Quiz.html

9. Simulated Annealing

1. What is simulated annealing

2. Simulated Annealing Quiz.html

10. Simulated Annealing Implementation - Continuous Functions

1. Simulated annealing implementation I

2. Simulated annealing implementation II

3.1 simulatedannealingfunction.zip

3. Simulated annealing implementation III

11. Simulated Annealing Implementation - Combinatorial Optimization

1. What is the travelling salesman problem (TSP)

2. Travelling salesman problem implementation I

3. Travelling salesman problem implementation II

4. Travelling salesman problem implementation III

5.1 simulatedannealingtsp.zip

5. Travelling salesman problem implementation IV

12. Simulated Annealing Implementation - Sudoku

1. What is the Sudoku problem

2. Sudoku problem implementation I

3. Sudoku problem implementation II

4. Sudoku problem implementation III

5.1 simulatedannealingsudoku.zip

5. Sudoku problem implementation IV

13. Genetic Algorithms

1. Genetic algorithms introduction - basics

2. Genetic algorithms introduction - chromosomes

3. Genetic algorithms introduction - crossover

4. Genetic algorithms introduction - mutation

5. Genetic algorithms introduction - selection

6. Genetic algorithms introduction - the algorithm

7. What is elitism

8. Advantages and limitations of genetic algorithms

9. Genetic Algorithms Quiz.html

14. Genetic Algorithms Implementation - Simple Example

1. Genetic algorithm implementation I

2. Genetic algorithm implementation II

3. Genetic algorithm implementation III

4. Genetic algorithm implementation IV

5.1 geneticalgorithmelitism.zip

5. Genetic algorithm implementation V - elitism

15. Genetic Algorithms Implementation - Constraint Satisfaction Problems

1. What is the N-queens problem

2. N queens problem implementation I

3.1 geneticalgorithmnqueens.zip

3. N queens problem implementation II

16. Challenge #3 - Knapsack Problem

1. Course challenge #3 - knapsack problem overview.html

2. What is the knapsack problem

3.1 geneticalgorithmknapsack.zip

3. Knapsack problem implementation

17. Particle Swarm Optimization

1. What is swarm intelligence

2. Particle swarm optimization introduction - basics

3. Particle swarm optimization introduction - the algorithm

4. Exploration and exploitation trade-off

5. Particle Swarm Optimization Quiz.html

18. Particle Swarm Optimization - Simple Example

1. Particle swarm optimization implementation I

2. Particle swarm optimization implementation II

3.1 particleswarmoptimizationfunction.zip

3. Particle swarm optimization implementation III

19. ### TWO PLAYER GAMES ###

1. Game trees introduction

2. Two Player Games Quiz.html

20. Minimax Algorithm - Game Engines

1. Minimax algorithm introduction - basics

2. Minimax algorithm introduction - the algorithm

3. Minimax algorithm introduction - relation to tic-tac-toe

4. Alpha-beta pruning introduction

5. Alpha-beta pruning example

6. Chess problem

7. Game Engines Quiz.html

21. Tic-Tac-Toe Game

1. Tic tac toe implementation I

2. Tic tac toe implementation II

3. Tic tac toe implementation III

4. Tic tac toe implementation IV

5. Tic tac toe implementation V

6.1 minimaxtictactoe.zip

6. Tic tac toe implementation VI

7. Minimax stack memory visualization

8.1 minimaxpruningtictactoe.zip

8. Tic tac toe implementation VII - pruning

22. ### REINFORCEMENT LEARNING ###

1. What is reinforcement learning.html

2. Applications of reinforcement learning

23. Markov Decision Process (MDP) Theory

1. Markov decision processes basics I

2. Markov decision processes basics II

3. Markov decision processes - equations

4. Markov decision processes - illustration

5. Bellman-equation

6. How to solve MDP problems

7. What is value iteration

8. What is policy iteration

9. Mathematical formulation of reinforcement learning.html

10. Reinforcement Learning Basics Quiz.html

24. Exploration vs. Exploitation Problem

1. Exploration vs exploitation problem

2. N-armed bandit problem introduction

3. N-armed bandit problem implementation

4. Applications AB testing in marketing

5. Exploration vs. Exploitation Quiz.html

25. Q Learning Theory

1. What is Q learning

2. Q learning introduction - the algorithm

3. Q learning illustration

4. Mathematical formulation of Q learning.html

5. Q Learning Quiz.html

26. Q Learning Implementation (Tic Tac Toe)

1. Tic tac toe with Q learning implementation I

2. Tic tac toe with Q learning implementation II

3. Tic tac toe with Q learning implementation III

4. Tic tac toe with Q learning implementation IV

5. Tic tac toe with Q learning implementation V

6. Tic tac toe with Q learning implementation VI

7. Tic tac toe with Q learning implementation VII

8.1 qlearningtictactoe.zip

8. Tic tac toe with Q learning implementation VIII

27. ### APPENDIX - PYTHON PROGRAMMING CRASH COURSE ###

1. Python crash course introduction.html

28. Appendix #1 - Python Basics

1. First steps in Python

2. What are the basic data types

3. Booleans

4. Strings

5. String slicing

6. Type casting

7. Operators

8. Conditional statements

9. How to use multiple conditions

10. Exercise conditional statements.html

11. Solution conditional statements.html

12. Logical operators

13. Loops - for loop

14. Loops - while loop

15. Exercise calculating the average.html

16. Solution calculating the average.html

17. What are nested loops

18. Enumerate

19. Break and continue

20. Calculating Fibonacci-numbers

21. Exercise Fibonacci-numbers.html

22. Solution Fibonacci-numbers.html

29. Appendix #2 - Functions

1. What are functions

2. Defining functions

3. Positional arguments and keyword arguments

4. Returning values

5. Returning multiple values

6. Exercise functions.html

7. Solution functions.html

8. Yield operator

9. Local and global variables

10. What are the most relevant built-in functions

11. What is recursion

12. Exercise recursion.html

13. Solution recursion.html

14. Local vs global variables

15. The __main__ function

30. Appendix #3 - Data Structures in Python

1. How to measure the running time of algorithms

2. Data structures introduction

3. What are array data structures I

4. What are array data structures II

5. Lists in Python

6. Lists in Python - advanced operations

7. Lists in Python - list comprehension

8. (!!!) Python lists and arrays.html

9. Exercise list comprehension.html

10. Solution list comprehension.html

11. Measuring running time of lists.html

12. What are tuples

13. Mutability and immutability

14. What are linked list data structures

15. Doubly linked list implementation in Python

16. Hashing and O(1) running time complexity

17. Dictionaries in Python

18. Sets in Python

19. Exercise constructing dictionaries.html

20. Solution constructing dictionaries.html

21. Sorting

31. Appendix #4 - Object Oriented Programming (OOP)

1. What is object oriented programming (OOP)

2. Class and objects basics

3. Using the constructor

4. Class variables and instance variables

5. Exercise constructing classes.html

6. Solution constructing classes.html

7. Private variables and name mangling

8. What is inheritance in OOP

9. The super keyword

10. Function (method) override

11. What is polymorphism

12. Polymorphism and abstraction example

13. Exercise abstraction.html

14. Solution abstraction.html

15. Modules

16. The __str__ function

17. Comparing objects - overriding functions

32. Appendix #5 - NumPy

1. What is the key advantage of NumPy

2. Creating and updating arrays

3. Dimension of arrays

4. Indexes and slicing

5. Types

6. Reshape

7. Exercise reshape problem.html

8. Solution reshape problem.html

9. Stacking and merging arrays

10. Filter

11. Running time comparison arrays and lists.html

33. COURSE MATERIALS

1.1 meta_heuristics_ai.zip

1. Slides and source code.html

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