Recursion, Backtracking and Dynamic Programming in Java

Name: Recursion, Backtracking and Dynamic Programming in Java
Price: 9 USD
Availability: InStock

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.

Watch Online Recursion, Backtracking and Dynamic Programming in Java

Join premium to watch

Go to premium

#	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