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This course is about the fundamental concepts of algorithmic problems, focusing on backtracking and dynamic programming. 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 research & development.
The first chapter is about backtracking: we will talk about problems such as Nqueens problem or hamiltonian cycles,Â coloring problem andÂ Sudoku problem. In the second chapter we will talk about dynamic programming, theory then the concrete examples one by one: fibonacci sequence problem and knapsack 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 Java.
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Section 1: Introduction  

Lecture 1 
Introduction
Preview

01:54  
Lecture 2 
Complexity theory

Article  
Section 2: Recursion  
Lecture 3 
Recursion introduction
Preview

07:00  
Lecture 4  03:13  
Note: we can usually solve a problem either with recursion or with iteration. Several algorithms  especially the divide and conquer ones  rely heavily on recursive method calls. 

Lecture 5 
House building problem

02:56  
Lecture 6 
Factorial function

03:21  
Lecture 7 
Euclidean algorithm  greatest common divisor

02:42  
Lecture 8 
Linear and binary search

06:59  
Lecture 9 
Towers of Hanoi problem introduction

03:46  
Lecture 10 
Tower of Hanoi problem implementation

02:16  
Section 3: Selection Algorithms  
Lecture 11 
Selection algorithms introduction

05:21  
Lecture 12 
Quickselect introduction  Hoare algorithm

08:18  
Lecture 13 
Quickselect simulation

08:25  
Lecture 14 
Quickselect implementation

05:45  
Lecture 15 
Advanced selection  median of medians, introselect

07:21  
Lecture 16 
Online selection  the secretary problem

04:58  
Section 4: Backtracking  
Lecture 17 
Backtracking introduction

03:11  
Lecture 18 
Nqueens problem introduction

07:38  
Lecture 19 
Nqueens problem implementation

12:09  
Lecture 20 
Hamiltonian cycle introduction

11:29  
Lecture 21 
Hamiltonian problem  NPhard problems

04:17  
Lecture 22 
Hamiltonian cycle implementation

13:02  
Lecture 23 
Coloring problem introduction

09:54  
Lecture 24 
Coloring problem implementation

18:03  
Lecture 25 
Knight tour introduction

02:55  
Lecture 26 
Knight tour implementation

05:49  
Lecture 27 
Maze problem introduction

02:52  
Lecture 28 
Maze problem implementation

05:44  
Lecture 29 
Sudoku introduction

04:47  
Lecture 30 
Sudoku implementation

10:06  
Section 5: Dynamic Programming  
Lecture 31 
Dynamic programming introduction

02:25  
Lecture 32 
Fibonacci numbers introduction

04:15  
Lecture 33 
Fibonacci numbers implementation

09:24  
Lecture 34 
Knapsack problem introduction

06:40  
Lecture 35 
Knapsack problem example

14:42  
Lecture 36 
Knapsack problem implementation

16:02  
Lecture 37 
Coin change problem introduction

14:54  
Lecture 38 
Coin change problem implementation

08:58  
Lecture 39 
Rod cutting problem introduction

12:41  
Lecture 40 
Rod cutting problem implementation

04:09  
Lecture 41 
Subset sum problem introduction

12:51  
Lecture 42 
Subset sum problem implementation

05:29  
Section 6: Other Algorithmic Problems  
Lecture 43 
Bin packing problem introduction

05:31  
Lecture 44 
Bin packing problem implementation

07:29  
Lecture 45 
Closest pair of points problem introduction

09:40  
Lecture 46 
Closest pair of points problem implementation

10:52  
Section 7: Numerical Methods  
Lecture 47 
Root of functions introduction

04:42  
Lecture 48 
Finding roots of functions: NewtonRaphson method

03:33  
Lecture 49 
Integration  trapezoidal method introduction

07:07  
Lecture 50 
Integration  trapezoidal method implementation

04:25  
Lecture 51 
Integration  Simpson method introduction

02:50  
Lecture 52 
Integration  Simpson method implementation

05:32  
Lecture 53 
Integeration  Monte Carlo method introduction

05:24  
Lecture 54 
Integration  Monte Carlo method implementation

06:32  
Section 8: Source Code  
Lecture 55 
Source code

Article  
Lecture 56 
Slides

Article  
Lecture 57 
Coupon codes  get any of my other courses for a discounted price

Article 
Hi!
My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the BlackScholes model, or the Mertonmodel. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.