Recursion, Backtracking and Dynamic Programming in Java

9h 46m 17s
English
Paid
July 9, 2024

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.

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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?

  • 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 - BACKTRACKING

  • what is backtracking?

  • n-queens problem

  • Hamiltonian cycle problem

  • coloring problem

  • knight's tour problem

  • Sudoku game

Section 5 - DYNAMIC PROGRAMMING

  • what is dynamic programming?

  • knapsack problem

  • 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

Section 8 - COMMON INTERVIEW QUESTIONS

  • top interview questions (Google, Facebook and Amazon)

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 Java.

Finally, YOU CAN LEARN ABOUT THE MOST COMMON INTERVIEW QUESTIONS (Google, MicroSoft, Amazon etc.)

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# Title Duration
1 Introduction 02:09
2 What are stack and heap memory? 03:45
3 Stack memory and heap memory simulation 06:15
4 Recursion introduction 08:44
5 Adding numbers: iteration vs recursion 05:05
6 Recursion and stack memory (stack overflow) 10:10
7 Head recursion and tail recursion implementation 06:39
8 Factorial function - head recursion 06:07
9 Factorial problem - visualizing the stack 04:15
10 Factorial function - tail recursion 05:48
11 Fibonacci numbers - head recursion 04:58
12 Towers of Hanoi problem introduction 06:01
13 Tower of Hanoi problem implementation 05:39
14 Towers of Hanoi - visualizing the stack 07:09
15 Iteration and recursion revisited 01:43
16 What is linear search? 01:39
17 Linear search implementation 03:17
18 What is binary (logarithmic) search? 03:49
19 Binary search implementation 08:54
20 Selection algorithms introduction 06:44
21 Quickselect introduction - Hoare algorithm 09:49
22 Quickselect visualization 08:52
23 Quickselect implementation 11:57
24 What the problem with pivots? 05:50
25 Advanced selection - median of medians algorithm 07:31
26 Combining algorithms - introselect algorithm 01:23
27 Online selection - the secretary problem 08:02
28 Backtracking introduction 05:51
29 Brute-force search and backtracking 04:03
30 N-queens problem introduction 08:07
31 What is the search tree? 03:10
32 N-queens problem implementation I 09:22
33 N-queens problem implementation II 06:17
34 N-queens problem and stack memory visualization 06:22
35 Hamiltonian paths (and cycles) introduction 08:03
36 Hamiltonian cycle illustration 04:42
37 Hamiltonian cycle implementation I 10:17
38 Hamiltonian cycle implementation II 07:02
39 Coloring problem introduction 08:49
40 Coloring problem visualization 04:33
41 Coloring problem implementation I 07:18
42 Coloring problem implementation II 05:25
43 Knight's tour introduction 03:55
44 Knight's tour implementation I 10:33
45 Knight's tour implementation II 04:49
46 Maze problem introduction 04:59
47 Maze problem implementation I 07:22
48 Maze problem implementation II 06:53
49 Sudoku introduction 05:52
50 Sudoku implementation I 08:38
51 Sudoku implementation II 03:28
52 What is the issue with NP-complete problems? 05:05
53 Dynamic programming introduction 08:24
54 Fibonacci numbers introduction 03:31
55 Fibonacci numbers implementation 08:36
56 Knapsack problem introduction 18:08
57 Knapsack problem example 12:59
58 Knapsack problem implementation I 08:08
59 Knapsack problem implementation II 04:27
60 Rod cutting problem introduction 08:04
61 Rod cutting problem example 11:07
62 Rod cutting problem implementation 08:46
63 Subset sum problem introduction 10:12
64 Subset sum problem example 09:09
65 Subset sum problem implementation 09:05
66 Bin packing problem introduction 08:10
67 Bin packing problem implementation 06:53
68 What are divide-and-conquer approaches? 09:24
69 Binary search revisited 01:46
70 Merge sort theory 08:18
71 Merge sort implementation 06:27
72 Closest pair of points problem introduction I 15:21
73 Closes pair of points problem introduction II 04:15
74 Closest pair of points implementation 23:54
75 Palindrome problem solution 08:01
76 Integer reversion solution 07:01
77 Two eggs problem solution I 08:02
78 Two eggs problem solution II 11:40
79 Duplicates in an array problem solution 07:57
80 Anagram problem solution 03:52
81 Largest sum subarray problem solution 10:49
82 What is Algorhyme? 00:42

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