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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?
tower of Hanoi problem
Section 2 - SEARCH ALGORITHMS
linear search approach
binary search algorithm
Section 3 - SELECTION ALGORITHMS
what are selection algorithms?
how to find the k-th order statistics in O(N) linear running time?
median of medians algorithm
the secretary problem
Section 4 - BACKTRACKING
what is backtracking?
Hamiltonian cycle problem
knight's tour problem
Section 5 - DYNAMIC PROGRAMMING
what is dynamic programming?
rod cutting problem
subset sum problem
Section 6 - OPTIMAL PACKING
what is optimal packing?
bin packing problem
Section 7 - 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
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.
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