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Learn Recursion, Backtracking, Divide and Conquer Methods and Dynamic programming via Examples and Problems in Python
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Recursion, Backtracking and Dynamic Programming in Python
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 – RECURSIONwhat are recursion and recursive methodsstack memory and heap memory overviewwhat is stack overflow?Fibonacci numbersfactorial functiontower of Hanoi problemSection 2 – SEARCHALGORITHMSlinear search approachbinary search algorithmSection 3 – SELECTIONALGORITHMSwhat are selection algorithms?how to find the k-th order statistics in O(N) linear running time?quickselect algorithmmedian of medians algorithmthe secretary problemSection 4 – BACKTRACKINGwhat is backtracking?n-queens problemHamiltonian cycle problemcoloring problemknight’s tour problemmaze problemSection 5 – DYNAMICPROGRAMMINGwhat is dynamic programming?knapsack problemrod cutting problemsubset sum problemSection 6 – OPTIMALPACKINGwhat is optimal packing?bin packing problemSection 7 – DIVIDEANDCONQUERAPPROACHESwhat is the divide and conquer approach?dynamic programming and divide and conquer methodhow to achieve sorting in O(NlogN) with merge sort?the closest pair of points problemIn 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!
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