Specialized course to teach you how to solve recursive and dynamic programming problems in coding interviews
What you will learn
Have an intuitive framework that can be used to solve recursive you have not seen before
Understand two main ways of thinking about recursive problems: self-similar or decision trees
Know when and how to apply dynamic programming optimization to recursive algorithms
Solve recursive and dynamic programming problems in coding interviews
Description
When I was preparing for coding interviews a while back, I found recursion and dynamic programming to be among the hardest topics covered. It felt like it took me weeks to get good at solving these types of problems. After I finished preparing for interviews, I thought there must be an easier way for someone to learn how to solve these types of problems, so they can learn this much quicker. After solving more than 100 recursive and dynamic programming problems, I created a problem-solving framework that you can use to solve these types of problems.
This course teaches you this framework by first splitting up all recursive algorithms into two types: self-similar algorithms and decision tree algorithms. Self-similar algorithms solve problems where there is an easy to see self-similar relationship with the problem itself we are solving for, and decision tree algorithms solve problems by ‘trying’ every possible decision sequence in a system. We will spend time building a strong foundation on how to solve problems with recursion (even problems that are typically referred to as dynamic programming problems), and then, with a strong foundation, we will learn how to improve our recursive algorithms with dynamic programming.
Prerequisites:
- The course uses Python, requiring only a basic understanding of the language.
- A basic understanding of non-recursive space and time complexity.
- A basic understanding of non-recursive data structures and algorithms, such as hash tables and tree structures, is required.
Content