AI and Meta-Heuristics (Combinatorial Optimization) Python

Focused View

Holczer Balazs

17:23:17

298 View

1. Introduction

1. Introduction.mp4

03:23

2.1 meta_heuristics_ai.zip

2. Slides and source code.html

2. ### GRAPH ALGORITHMS ###

1. Why to consider graph algorithms.mp4

03:43

3. Breadth-First Search (DFS) Algorithm

1. What is breadth-first search.mp4

08:10

2.1 breadthfirstsearch.zip

2. Breadth-first search implementation.mp4

09:48

3. Applications of breadth-first search.mp4

02:19

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).mp4

05:49

3.1 webcrawler.zip

3. WebCrawler basic implementation.mp4

09:39

5. Depth-First Search (DFS) Algorithm

1. What is depth-first search.mp4

08:52

2.1 depthfirstsearch.zip

2. Depth-first search implementation.mp4

05:55

3.1 depthfirstsearchrecursion.zip

3. Depth-first search implementation with recursion.mp4

02:52

4. Depth-first search and stack memory visualization.mp4

06:01

5. Memory comparison of graph traversal algorithms.mp4

04:18

6. Applications of depth-first search.mp4

03:02

7. Depth-First Search Quiz.html

6. Challange #2 - Maze Solver

1. Course challenge #2 - maze problem.html

2. Maze problem introduction.mp4

04:55

3.1 mazeproblem.zip

3. Maze problem implementation.mp4

13:07

4. Maze problem stack memory visualization.mp4

06:39

7. A Search Algorithm

1. What is the A search algorithm.mp4

09:54

2. A search illustration.mp4

08:51

3. A search implementation I.mp4

07:19

4. A search implementation II.mp4

08:47

5.1 astarsearch.zip

5. A search implementation III.mp4

02:49

6. Path finding algorithms comparison.mp4

02:23

7. A Search Quiz.html

8. ### META-HEURISTICS ###

1. What are meta-heuristic approaches.mp4

08:48

2. Heuristics Quiz.html

9. Simulated Annealing

1. What is simulated annealing.mp4

09:41

2. Simulated Annealing Quiz.html

10. Simulated Annealing Implementation - Continuous Functions

1. Simulated annealing implementation I.mp4

04:54

2. Simulated annealing implementation II.mp4

07:37

3.1 simulatedannealingfunction.zip

3. Simulated annealing implementation III.mp4

06:17

11. Simulated Annealing Implementation - Combinatorial Optimization

1. What is the travelling salesman problem (TSP).mp4

06:14

2. Travelling salesman problem implementation I.mp4

07:56

3. Travelling salesman problem implementation II.mp4

06:32

4. Travelling salesman problem implementation III.mp4

08:40

5.1 simulatedannealingtsp.zip

5. Travelling salesman problem implementation IV.mp4

04:21

12. Simulated Annealing Implementation - Sudoku

1. What is the Sudoku problem.mp4

07:21

2. Sudoku problem implementation I.mp4

13:12

3. Sudoku problem implementation II.mp4

13:22

4. Sudoku problem implementation III.mp4

06:54

5.1 simulatedannealingsudoku.zip

5. Sudoku problem implementation IV.mp4

02:59

13. Genetic Algorithms

1. Genetic algorithms introduction - basics.mp4

05:30

2. Genetic algorithms introduction - chromosomes.mp4

04:19

3. Genetic algorithms introduction - crossover.mp4

06:37

4. Genetic algorithms introduction - mutation.mp4

05:31

5. Genetic algorithms introduction - selection.mp4

04:57

6. Genetic algorithms introduction - the algorithm.mp4

02:31

7. What is elitism.mp4

03:54

8. Advantages and limitations of genetic algorithms.mp4

05:46

9. Genetic Algorithms Quiz.html

14. Genetic Algorithms Implementation - Simple Example

1. Genetic algorithm implementation I.mp4

06:35

2. Genetic algorithm implementation II.mp4

03:39

3. Genetic algorithm implementation III.mp4

13:22

4. Genetic algorithm implementation IV.mp4

04:21

5.1 geneticalgorithmelitism.zip

5. Genetic algorithm implementation V - elitism.mp4

06:00

15. Genetic Algorithms Implementation - Constraint Satisfaction Problems

1. What is the N-queens problem.mp4

07:35

2. N queens problem implementation I.mp4

04:31

3.1 geneticalgorithmnqueens.zip

3. N queens problem implementation II.mp4

10:30

16. Challenge #3 - Knapsack Problem

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

2. What is the knapsack problem.mp4

05:36

3.1 geneticalgorithmknapsack.zip

3. Knapsack problem implementation.mp4

13:41

17. Particle Swarm Optimization

1. What is swarm intelligence.mp4

06:52

2. Particle swarm optimization introduction - basics.mp4

09:33

3. Particle swarm optimization introduction - the algorithm.mp4

10:24

4. Exploration and exploitation trade-off.mp4

06:31

5. Particle Swarm Optimization Quiz.html

18. Particle Swarm Optimization - Simple Example

1. Particle swarm optimization implementation I.mp4

09:59

2. Particle swarm optimization implementation II.mp4

14:25

3.1 particleswarmoptimizationfunction.zip

3. Particle swarm optimization implementation III.mp4

03:45

19. ### TWO PLAYER GAMES ###

1. Game trees introduction.mp4

08:24

2. Two Player Games Quiz.html

20. Minimax Algorithm - Game Engines

1. Minimax algorithm introduction - basics.mp4

08:19

2. Minimax algorithm introduction - the algorithm.mp4

06:32

3. Minimax algorithm introduction - relation to tic-tac-toe.mp4

06:47

4. Alpha-beta pruning introduction.mp4

09:06

5. Alpha-beta pruning example.mp4

08:27

6. Chess problem.mp4

04:10

7. Game Engines Quiz.html

21. Tic-Tac-Toe Game

1. Tic tac toe implementation I.mp4

07:37

2. Tic tac toe implementation II.mp4

03:56

3. Tic tac toe implementation III.mp4

06:54

4. Tic tac toe implementation IV.mp4

07:59

5. Tic tac toe implementation V.mp4

05:40

6.1 minimaxtictactoe.zip

6. Tic tac toe implementation VI.mp4

05:58

7. Minimax stack memory visualization.mp4

08:04

8.1 minimaxpruningtictactoe.zip

8. Tic tac toe implementation VII - pruning.mp4

03:54

22. ### REINFORCEMENT LEARNING ###

1. What is reinforcement learning.html

2. Applications of reinforcement learning.mp4

02:44

23. Markov Decision Process (MDP) Theory

1. Markov decision processes basics I.mp4

05:38

2. Markov decision processes basics II.mp4

06:21

3. Markov decision processes - equations.mp4

12:00

4. Markov decision processes - illustration.mp4

07:49

5. Bellman-equation.mp4

05:41

6. How to solve MDP problems.mp4

02:20

7. What is value iteration.mp4

06:29

8. What is policy iteration.mp4

03:52

9. Mathematical formulation of reinforcement learning.html

10. Reinforcement Learning Basics Quiz.html

24. Exploration vs. Exploitation Problem

1. Exploration vs exploitation problem.mp4

03:29

2. N-armed bandit problem introduction.mp4

08:46

3. N-armed bandit problem implementation.mp4

11:12

4. Applications AB testing in marketing.mp4

04:11

5. Exploration vs. Exploitation Quiz.html

25. Q Learning Theory

1. What is Q learning.mp4

05:44

2. Q learning introduction - the algorithm.mp4

07:08

3. Q learning illustration.mp4

11:06

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.mp4

03:43

2. Tic tac toe with Q learning implementation II.mp4

07:36

3. Tic tac toe with Q learning implementation III.mp4

07:24

4. Tic tac toe with Q learning implementation IV.mp4

07:26

5. Tic tac toe with Q learning implementation V.mp4

04:54

6. Tic tac toe with Q learning implementation VI.mp4

12:05

7. Tic tac toe with Q learning implementation VII.mp4

06:21

8.1 qlearningtictactoe.zip

8. Tic tac toe with Q learning implementation VIII.mp4

05:56

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

1. Python crash course introduction.html

28. Appendix #1 - Python Basics

1. First steps in Python.mp4

05:49

2. What are the basic data types.mp4

04:45

3. Booleans.mp4

02:08

4. Strings.mp4

07:44

5. String slicing.mp4

06:47

6. Type casting.mp4

04:20

7. Operators.mp4

05:23

8. Conditional statements.mp4

04:41

9. How to use multiple conditions.mp4

08:07

10. Exercise conditional statements.html

11. Solution conditional statements.html

12. Logical operators.mp4

04:04

13. Loops - for loop.mp4

06:00

14. Loops - while loop.mp4

04:13

15. Exercise calculating the average.html

16. Solution calculating the average.html

17. What are nested loops.mp4

02:55

18. Enumerate.mp4

03:51

19. Break and continue.mp4

05:32

20. Calculating Fibonacci-numbers.mp4

02:30

21. Exercise Fibonacci-numbers.html

22. Solution Fibonacci-numbers.html

29. Appendix #2 - Functions

1. What are functions.mp4

04:07

2. Defining functions.mp4

05:24

3. Positional arguments and keyword arguments.mp4

10:30

4. Returning values.mp4

02:26

5. Returning multiple values.mp4

03:14

6. Exercise functions.html

7. Solution functions.html

8. Yield operator.mp4

05:02

9. Local and global variables.mp4

02:12

10. What are the most relevant built-in functions.mp4

04:26

11. What is recursion.mp4

09:29

12. Exercise recursion.html

13. Solution recursion.html

14. Local vs global variables.mp4

04:16

15. The __main__ function.mp4

03:25

30. Appendix #3 - Data Structures in Python

1. How to measure the running time of algorithms.mp4

10:00

2. Data structures introduction.mp4

03:17

3. What are array data structures I.mp4

06:55

4. What are array data structures II.mp4

06:56

5. Lists in Python.mp4

05:43

6. Lists in Python - advanced operations.mp4

08:27

7. Lists in Python - list comprehension.mp4

05:56

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.mp4

03:58

13. Mutability and immutability.mp4

04:30

14. What are linked list data structures.mp4

08:13

15. Doubly linked list implementation in Python.mp4

05:32

16. Hashing and O(1) running time complexity.mp4

08:03

17. Dictionaries in Python.mp4

09:41

18. Sets in Python.mp4

09:49

19. Exercise constructing dictionaries.html

20. Solution constructing dictionaries.html

21. Sorting.mp4

10:44

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

1. What is object oriented programming (OOP).mp4

02:18

2. Class and objects basics.mp4

03:00

3. Using the constructor.mp4

06:00

4. Class variables and instance variables.mp4

04:46

5. Exercise constructing classes.html

6. Solution constructing classes.html

7. Private variables and name mangling.mp4

04:31

8. What is inheritance in OOP.mp4

03:49

9. The super keyword.mp4

04:24

10. Function (method) override.mp4

02:34

11. What is polymorphism.mp4

04:25

12. Polymorphism and abstraction example.mp4

06:10

13. Exercise abstraction.html

14. Solution abstraction.html

15. Modules.mp4

06:00

16. The __str__ function.mp4

03:16

17. Comparing objects - overriding functions.mp4

08:00

32. Appendix #5 - NumPy

1. What is the key advantage of NumPy.mp4

04:12

2. Creating and updating arrays.mp4

07:36

3. Dimension of arrays.mp4

09:12

4. Indexes and slicing.mp4

07:59

5. Types.mp4

04:43

6. Reshape.mp4

07:53

7. Exercise reshape problem.html

8. Solution reshape problem.html

9. Stacking and merging arrays.mp4

06:17

10. Filter.mp4

03:39

11. Running time comparison arrays and lists.html

33. COURSE MATERIALS

1.1 meta_heuristics_ai.zip

1. Slides and source code.html

Description

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

Who is this for?

Beginner Python programmers curious about artificial intelligence and combinatorial optimization

More details

Description
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 veryÂ good guess about stock priceÂ movement in the market.
### PATHFINDINGÂ 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-HEURISTICSÂ ###
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
### GAMESÂ ANDÂ GAMEÂ TREESÂ ###
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
### REINFORCEMENTÂ 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
### PYTHONÂ PROGRAMMINGÂ CRASH COURSEÂ ###
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!
Who this course is for:
Beginner Python programmers curious about artificial intelligence and combinatorial optimization

User Reviews

Rating

average 0

Total votes0

Focused display

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model.Take a look at my website if you are interested in these topics!

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.