This course is about the fundamental concepts of algorithmic problems focusing on recursion, backtracking, dynamic programming and divide and conquer approaches. As far as I am concerned, these techniques are very important nowadays, algorithms can be used (and have several applications) in several fields from software engineering to investment banking or R&D.

Section 1 - RECURSION

• what are recursion and recursive methods

• stack memory and heap memory overview

• what is stack overflow?

• Fibonacci numbers

• factorial function

• tower of Hanoi problem

Section 2 - SEARCH ALGORITHMS

• linear search approach

• binary search algorithm

Section 3 - SELECTION ALGORITHMS

• what are selection algorithms?

• Hoare's algorithm

• how to find the k-th order statistics in O(N) linear running time?

• quickselect algorithm

• median of medians algorithm

• the secretary problem

Section 4 - BIT MANIPULATION PROBLEMS

• binary numbers

• logical operators and shift operators

• checking even and odd numbers

• bit length problem

• Russian peasant multiplication

Section 5 - BACKTRACKING

• what is backtracking?

• n-queens problem

• Hamiltonian cycle problem

• coloring problem

• knight's tour problem

• maze problem

• Sudoku problem

Section 6 - DYNAMIC PROGRAMMING

• what is dynamic programming?

• knapsack problem

• rod cutting problem

• subset sum problem

• Kadane's algorithm

• longest common subsequence (LCS) problem

Section 7 - OPTIMAL PACKING 

• what is optimal packing?

• bin packing problem

Section 8 - DIVIDE AND CONQUER APPROACHES

• what is the divide and conquer approach?

• dynamic programming and divide and conquer method

• how to achieve sorting in O(NlogN) with merge sort?

• the closest pair of points problem

Section 9 - Substring Search Algorithms

• substring search algorithms

• brute-force substring search

• Z substring search algorithm

• Rabin-Karp algorithm and hashing

• Knuth-Morris-Pratt (KMP) substring search algorithm

Section 10 - COMMON INTERVIEW QUESTIONS

• top interview questions (Google, Facebook and Amazon)

• anagram problem

• palindrome problem

• integer reversion problem

• dutch national flag problem

• trapping rain water problem

Section 11 - Algorithms Analysis

• how to measure the running time of algorithms

• running time analysis with big O (ordo), big Ω (omega) and big θ (theta) notations

• complexity classes

• polynomial (P) and non-deterministic polynomial (NP) algorithms

In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together from scratch in Python.

Thanks for joining the course, let's get started!