Advanced Algorithms in Java
4.5 (233 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
3,840 students enrolled
Wishlisted Wishlist

Please confirm that you want to add Advanced Algorithms in Java to your Wishlist.

Add to Wishlist

Advanced Algorithms in Java

Breadth-first search, depth-first search, shortest path, arbitrage, strongly connected components and graph algorithms
Best Seller
4.5 (233 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
3,840 students enrolled
Created by Holczer Balazs
Last updated 8/2017
English
English
Current price: $10 Original price: $35 Discount: 71% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 10 hours on-demand video
  • 4 Articles
  • 4 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Learn about the applications of data structures
  • Implement advanced algorithms efficiently
  • Able to move towards advanced topics such as machine learning or big data analysis
  • Get a good grasp of algorithmic thinking
  • Get to know graph algorithms: BFS, DFS, shortest paths and spanning trees
View Curriculum
Requirements
  • Core Java
  • Eclipse IDE
  • Internet connection
  • Basic knowledge of data structures
Description

This course is about advanced algorithms focusing on graph traversal, shortest path problems, spanning trees and maximum flow problems and a lots of its applications from Google Web Crawler to taking advantage of stock market arbitrage situations.

The course is going to take approximately 11 hours to completely but I highly suggest you typing these algorithms out several times in order to get a good grasp of it. You can download the source code of the whole course at the last lecture.

In the first section we are going to talk about the main graph traversal algorithms (BFS, DFS) and its applications of course such as WebCrawler or topological ordering. The next section is about shortest path algorithms: there are several applications which we are going to be familiar with from image processing to FOREX arbitrage. The next chapter is about minimum spanning trees and clustering algorithms. Then, we are going to learn about the maximum flow problem, maybe the most important algorithm in this course. The last chapter is about how to solve NP problems such as the travelling salesman problem with simulated annealing.

You should definitely take this course if you are interested in advanced topics concerning algorithms. There are a bunch of fields where these methods can be used: from software engineering to scientific research.

Who is the target audience?
  • This course is meant for everyone from scientists to software developers who want to get closer to algorithmic thinking in the main
Compare to Other Java Algorithms Courses
Curriculum For This Course
78 Lectures
09:45:20
+
Introduction
3 Lectures 11:27

Graph theory introduction
07:53

+
Breadth-First Search
4 Lectures 42:24

BFS implementation
12:10

BFS - WebCrawler (core of search engines)
05:44

BFS - WebCrawler implementation
15:00
+
Depth-First Search
11 Lectures 01:30:51



Depth-first search introduction
10:21

DFS implementation I - with stack
11:28


Topological ordering introduction
10:31

Topological ordering implementation I
05:49

Topological ordering implementation II
06:07

Cycle detection introduction
06:30

Cycle detection implementation I
08:14

Cycle detection implementation II
07:21

Maze solving algorithm implementation
14:50

Memory management: BFS vs DFS
05:23

Graph traversal quiz
5 questions
+
Shortest Path Algorithms
14 Lectures 01:53:53
Dijkstra algorithm introduction - basics
05:35

Dijkstra algorithm introduction - algorithm
05:44

Dijkstra algorithm introduction - example
10:27

Bellman-Ford algorithm introduction
08:59

Dijkstra algorithm introduction - with adjacency matrix
09:22

Shortest path algorithms applications
08:37

Dijkstra algorithm implementation I
09:28

Dijkstra algorithm implementation II
10:57

Bellman-Ford algorithm implementation I
14:43

Bellman-Ford algorithm implementation II
06:05

DAG shortest path implementation
09:42

Arbitrage situations on FOREX introduction
03:47

Arbitrage situations on FOREX implementation
06:37

Longest path implementation
03:50

Shortest path algorithms quiz
5 questions
+
Spanning Trees
12 Lectures 01:57:15
Union-find data structure (disjoint sets)
10:56

Union-find data structure illustration
06:35

Spanning trees introduction - Kruskal algorithm
10:36

Kruskal algorithm implementation I
08:04

Kruskal algorithm implementation II - disjoint set
15:40

Kruskal algorithm implementation III
10:07

Spanning trees introduction - lazy Prim's algorithm
07:10

Prims lazy algorithm implementation I
11:38

Prims lazy algorithm implementation II - the core
07:06

Spanning trees introduction - eager Prim's algorithm
11:02

Eager Prim's algorithm implementation
10:45

Applications of spanning trees
07:36

Spanning trees quiz
5 questions
+
Strongly Connected Components
9 Lectures 50:53
Strongly connected components introduction
06:25

Kosaraju algorithm introduction
08:19

We create the helper classes and again, the Graph class. We transpose our directed graph inside this Graph class, because in Kosaraju algorithm we have to do the first DFS (topological ordering) in the transposed graph.

(transposing a graph is just to reverse each edge: startVertex will be targetVertex and targetVertex will be the startVertex)

Kosaraju implementation I - the Graph class
07:18

We create our class in which we will perform the first DFS on the transposed graph

Kosaraju implementation II
06:49

In this video we make sure the our Kosaraju implementation gives the right results

Kosaraju algorithm implementation III - test
02:40

Tarjan algorithm introduction
05:45

DFS is a robust but not so fast algorithm, so Tarjan algorithm tries to get rid of the DFS.

Tarjan implementation I
04:35

Tarjan implementation II - test
02:14

Applications of strongly connected components
06:48

Strongly connected components quiz
3 questions
+
Maximum Flow Problem
14 Lectures 01:26:11
Maximum flow introduction - basics
06:55

Maximum flow introduction - properties
09:30

Maximum flow introduction - cuts
05:13

Maximum flow introduction - residual networks
06:49

Maximum flow introduction - Ford-Fulkerson algorithm
04:50

Maximum flow introduction - example
08:35

Maximum flow introduction - applications
03:08

Maximum flow implementation I - Edge, Vertex
11:58

Maximum flow implementation II - FlowNetwork class
05:26

Maximum flow implementation III - Ford-Fulkerson algorithm
08:03

Maximum flow implementation IV - augmentation
06:43

Maximum flow implementation V - testing
02:55

Bipartite matching problem introduction
03:50

Bipartite matching implementation
02:16
+
Travelling Salesman Problem (TSP)
7 Lectures 01:01:27
Travelling salesman problem introduction
10:52

TSP implementation with simulated annealing I - city
09:51

TSP implementation with simulated annealing II - tour
13:10

TSP implementation with simulated annealing III - algorithm
10:17

TSP implementation with simulated annealing IV - testing
04:29

Tabu search introduction
08:29

Tabu search introduction II
04:19

Travelling salesman problem quiz
2 questions
+
Euler cycle - Chinese Postman Problem
1 Lecture 10:49
Euler's cycles introduction
10:49
+
Source Code & Slides
3 Lectures 00:09
Source code
00:01

You can download the slides here!


Slides
00:01

Coupon codes - get any of my other courses for a discounted price
00:06
About the Instructor
Holczer Balazs
4.4 Average rating
3,969 Reviews
39,329 Students
24 Courses
Software Engineer

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 Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.

Take a look at my website and join my email list if you are interested in these topics!