Algorithms

This course is a part of Computer science, a 6-course Topic series from Khan Academy.

We've partnered with Dartmouth college professors Tom Cormen and Devin Balkcom to teach introductory computer science algorithms, including searching, sorting, recursion, and graph theory. Learn with a combination of articles, visualizations, quizzes, and coding challenges.

This course contains 12 segments:

Intro to algorithms

What are algorithms and why should you care? We'll start with an overview of algorithms and then discuss two games that you could use an algorithm to solve more efficiently - the number guessing game and a route-finding game.

Binary search

Learn about binary search, a way to efficiently search an array of items by halving the search space each time.

Asymptotic notation

Learn how to use asymptotic analysis to describe the efficiency of an algorithm, and how to use asymptotic notation (Big O, Big-Theta, and Big-Omega) to more precisely describe the efficiency.

Selection sort

Learn selection sort, a simple algorithm for sorting an array of values, and see why it isn't the most efficient algorithm.

Insertion sort

Learn insertion sort, another simple but not very efficient way to sort an array of values.

Recursive algorithms

Learn the concept of recursion, a technique that is often used in algorithms. See how to use recursion to calculate factorial and powers of a number, plus to generate art.

Towers of Hanoi

Use the recursive technique to solve the Towers of Hanoi, a classic mathematical puzzle and one reportedly faced by monks in a temple.

Merge sort

Learn merge sort, a more efficient sorting algorithm that relies heavily on the power of recursion to repeatedly sort and merge sub-arrays.

Quick sort

Learn quick sort, another efficient sorting algorithm that uses recursion to more quickly sort an array of values.

Graph representation

Learn how to describe graphs, with their edges, vertices, and weights, and see different ways to store graph data, with edge lists, adjacency matrices, and adjacency lists.

Breadth-first search

Learn how to traverse a graph using breadth-first-search to find a particular node or to make sure you've visited all the nodes, traversing one layer at a time.

Further learning

Ideas of how you could continue your learning journey in algorithms.