Python Algorithms Course

Free hands-on course with the implementation (in Python) and description of several computational, mathematical and statistical algorithms.

Although it is not intended to have the formal rigor of a book, we tried to be as faithful as possible to the original algorithms and methods, only adding variants, when these were necessary for didactic purposes.

Quick Start

If you want to play with these notebooks online without having to install any library or configure hardware, you can use the following service:

What is an algorithm?

In mathematics and computer science, an algorithm is a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation.

Content

1. Algorithm's Basics

Greatest Common Divisor
Fibonacci Serie
Integer Factorization
Tower of Hanoi
Sorting Algorithm
Convex Hull - Greedy
Changing Distribution

2. Divide and Conquer

Binary Search
Quick Sort
Merge Sort
Convex Hull - Recursive

3. Graphs

Graph or Undirected Simple Graph
Graph Traversal
Minimum Spanning Tree
Eulerian Circuit and Path
Shortest Path
All-Pairs Shortest Path
Graph Coloring

4. Dynamic Programming

Binomial Coefficient
World Championship problem
Coin Change problem
The Knapsack problem
Longest Common Subsequence problem
Sequence Alignment problem

5. Probabilistic Algorithms

Pseudo-Random Number Generator
Primality Test
Monte Carlo Simulation
Metropolis-Hastings Algorithm
Las Vegas Algorithm
Law of Large Numbers

6. Similarity Functions

Common Similarity functions
Manual examples
Sklearn examples

7. Chaotic Systems

Chaotic Systems intro
Fractals

8. Cellular Automata

Linear Cellular Automata (LCA)
Reversible Linear Cellular Automata (RLCA)

Python Dependencies

  conda install -c anaconda numpy
  conda install -c anaconda pymc
  conda install -c anaconda networkx

Bibliography

G. Brassard, P. Bratley. (2006). Fundamentals of Algorithmics. Englewood Cliffs, New Jersey: Prentice-Hall, Inc.
R.C.T. Lee, S.S. Tseng, R.C. Chang, Y.T.Tsai. (2005). Introduction to the Design and Analysis of Algorithms. A Strategic Approach. Asia: McGraw-Hill Education.
K. Rosen. (2012). Discrete Mathematics and Its Applications. NewYork, NY: McGraw-Hill Education; Edición: 7th.

Contributing and Feedback

Any kind of feedback/suggestions would be greatly appreciated (algorithm design, documentation, improvement ideas, spelling mistakes, etc...). If you want to make a contribution to the course you can do it through a PR.

Author

Created by Andrés Segura-Tinoco
Created on May 17, 2019
Updated on Jun 26, 2021

License

This project is licensed under the terms of the MIT license.

sameerbr24/Algorithms-1