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What is an Algorithm? It is a set of rules that allows you to solve computational problems in an organized and definite order. You can also use algorithms on a roadmap to reach a destination.

This course is important to understand the basics in Algorithm as they are related to all the branches in computer science, development of technological innovations, quantum mechanics, economic market and facing new challenges on developing the current technologies.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

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Course Credit: Stanford University

Course Curriculum

Module 01
Why Study Algorithms 00:04:00
Algorithms – Integer Multiplication 00:09:00
Algorithms – Karatsuba Multiplication 00:13:00
Algorithms – “big-oh” notation and asymptotic analysis – About the Course 00:17:00
Algorithms – Merge Sort Motivation and Example 00:09:00
Algorithms – Merge Sort Pseudocode 00:13:00
Merge Sort Analysis 00:09:00
Guiding Principles for Analysis of Algorithms 00:15:00
Algorithms – The Gist 00:14:00
Algorithms – Big Oh Notation 00:04:00
Algorithms – Asimptotic Analysis – Basic Examples 00:07:00
Algorithms – Asimptotic Analysis – Big Omega and Theta 00:08:00
Algorithms – Asimptotic Analysis – Additional Examples Review 00:08:00
Module 02
Algorithms – On log n Algorithm for Counting Inversions I 00:13:00
Algorithms – On log n Algorithm for Counting Inversions II 00:17:00
Algorithms – Strassen’s Subcubic Matrix Multiplication Algorithm 00:23:00
Algorithms – On log n Algorithm for Closest Pair I Advanced Optional 00:32:00
Algorithms – On log n Algorithm for Closest Pair II Advanced Optional 00:19:00
Analyzing divide and conquer algorithms. – Motivation 00:08:00
Analyzing divide and conquer algorithms. – Formal Statement 00:10:00
Analyzing divide and conquer algorithms. – Examples 00:13:00
Analyzing divide and conquer algorithms. – Proof I 00:10:00
Algorithms – Analyzing divide and conquer algorithms. – Interpretation of the 3 Cases 00:11:00
Algorithms – Analyzing divide and conquer algorithms. – Proof II 00:16:00
Module 03
Algorithms – Quicksort -Overview 00:12:00
Algorithms – Quicksort – Partitioning Around a Pivot 00:25:00
Algorithms – Quicksort – Correctness of Quicksort Review Optional 00:11:00
Algorithms – Quicksort – Choosing a Good Pivot 00:22:00
Algorithms – Quicksort – Analysis I A Decomposition Principle 00:22:00
Algorithms – Quicksort – Analysis I A Decomposition Principle 00:12:00
Algorithms – Quicksort – Analysis III Final Calculations 00:09:00
Algorithms – Probability Review I 00:26:00
Algorithms – Probability Review II 00:17:00
Algorithms – Randomized Selection Algorithm 00:22:00
Algorithms – Randomized Selection Analysis 00:21:00
Algorithms – Deterministic Selection Algorithm 00:17:00
Algorithms – Deterministic Selection Analysis I 00:22:00
Algorithms – Deterministic Selection Analysis II 00:13:00
Module 04
Algorithms – Omega(n log n) Lower Bound for Comparison Based Sorting 00:14:00
Algorithms – Graphs and Minimum Cuts 00:16:00
Algorithms – Graph Representations 00:14:00
Algorithms – Random Contraction Algorithm 00:09:00
Algorithms – Analysis of Contraction Algorithm 00:30:00
Algorithms – Counting Minimum Cuts 00:07:00
Algorithms – Graph Search Overview 00:23:00
Algorithms – Breadth First Search BFS The Basics 00:14:00
Algorithms – BFS and Shortest Paths 00:08:00
Algorithms – BFS and Undirected Connectivity 00:13:00
Algorithms – Depth First Search DFS The Basics 00:07:00
Algorithms – Topological Sort 00:22:00
Algorithms – Computing Strong Components The Algorithm 00:29:00
Algorithms – Computing Strong Components The Analysis 00:26:00
Module 05
Algorithms – Structure of the Web 00:19:00
Algorithms – Dijkstra’s Shortest Path Algorithm 00:21:00
Algorithms – Dijkstra’s Algorithm Examples 00:13:00
Algorithms – Correctness of Dijkstra’s Algorithm 00:19:00
Algorithms – Dijkstra’s Algorithm Implementation and Running Time 00:26:00
Algorithms – Data Structures Overview 00:05:00
Algorithms – Heaps Implementation Details 00:21:00
Algorithms – Balanced Search Trees Operations and Applications 00:11:00
Algorithms – Binary Search Tree Basics, Part I 00:13:00
Algorithms – Binary Search Tree Basics, Part II 00:30:00
Algorithms – Red Black Trees 00:21:00
Module 06
Algorithms – Rotations Advanced 00:08:00
Algorithms – Insertion in a Red Black Tree 00:15:00
Algorithms – Hash Tables Operations and Applications 00:19:00
Algorithms – Hash Tables Implementation Details, Part I 00:19:00
Algorithms – Hash Tables Implementation Details, Part II 00:22:00
Algorithms – Pathological Data Sets and Universal Hashing Motivation 00:22:00
Algorithms – Universal Hashing Definition and Example Advanced Optional 00:26:00
Algorithms – Universal Hashing Analysis of Chaining Advanced Optional 00:19:00
Algorithms – Hash Table Performance with Open Addressing Advanced Optional 00:16:00
Algorithms – Bloom Filters The Basics 00:15:00
Algorithms – Bloom Filters Heuristic Analysis 00:13:00
Algorithms – Application Internet Routing 00:11:00
Algorithms – Application Sequence Alignment 00:09:00
Algorithms – Introduction to Greedy Algorithms 00:13:00
Algorithms – Application Optimal Caching 00:11:00
Algorithms – two motivating applications Problem Definition 00:06:00
Module 07
Algorithms – A Greedy Algorithm 00:13:00
Algorithms – Correctness Proof Part I 00:07:00
Algorithms – Correctness Proof Part II 00:05:00
Algorithms – Handling Ties 00:07:00
Algorithms – MST Problem Definition 00:11:00
Algorithms – Prim’s MST Algorithm 00:08:00
Algorithms – Correctness Proof I 00:16:00
Algorithms – Proof of Cut Property 00:12:00
Algorithms – Correctness Proof II 00:08:00
Algorithms – Fast Implementation I 00:15:00
Algorithms – Fast Implementation II 00:10:00
Algorithms – Kruskal’s MST Algorithm 00:07:00
Algorithms – Correctness of Kruskal’s Algorithm 00:09:00
Module 08
Algorithms – Implementing Kruskal’s Algorithm via Union Find I 00:09:00
Algorithms – Implementing Kruskal’s Algorithm via Union Find II 00:14:00
Algorithms – MSTs State of the Art and Open Questions 00:09:00
Algorithms – Application to Clustering 00:12:00
Algorithms – Correctness of Clustering Algorithm 00:10:00
Algorithms – Lazy Unions 00:10:00
Algorithms – Union by Rank 00:12:00
Algorithms – Analysis of Union by Rank 00:15:00
Algorithms – Path Compression 00:15:00
Algorithms – Path Compression The Hopcroft Ullman Analysis I 00:09:00
Algorithms – Path Compression The Hopcroft Ullman Analysis II 00:12:00
Module 09
Algorithms – The Ackermann Function 00:16:00
Algorithms – Path Compression Tarjan’s Analysis I 00:14:00
Algorithms – Path Compression Tarjan’s Analysis II 00:15:00
Algorithms – Huffman codes – Introduction and Motivation 00:09:00
Algorithms – Huffman codes – Problem Definition 00:10:00
Algorithms – Huffman codes – A Greedy Algorithm 00:17:00
Algorithms – Huffman codes – A More Complex Example 00:04:00
Algorithms – Huffman codes – Correctness Proof I 00:10:00
Algorithms – Huffman codes – Correctness Proof II 00:13:00
Module 10
Algorithms – Introduction Weighted Independent Sets in Path Graphs 00:08:00
Algorithms – WIS in Path Graphs Optimal Substructure 00:09:00
Algorithms – WIS in Path Graphs A Linear Time Algorithm 00:10:00
Algorithms – WIS in Path Graphs A Reconstruction Algorithm 00:07:00
Algorithms – Principles of Dynamic Programming 00:08:00
Algorithms – The Knapsack Problem 00:10:00
Algorithms – The Knapsack Problem – A Dynamic Programming Algorithm 00:10:00
Algorithms – The Knapsack Problem – example Review 00:13:00
Algorithms – Sequence Alignment – Optimal Substructure 00:14:00
Algorithms – Sequence Alignment – A Dynamic Programming Algorithm 00:12:00
Algorithms – optimal binary search trees – Problem Definition 00:12:00
Algorithms – optimal binary search trees – Optimal Substructure 00:10:00
Algorithms – optimal binary search trees – Proof of Optimal Substructure 00:07:00
Module 11
Algorithms – optimal binary search trees – A Dynamic Programming Algorithm I 00:10:00
Algorithms – optimal binary search trees – A Dynamic Programming Algorithm II 00:09:00
Algorithms – Single Source Shortest Paths, Revisted 00:11:00
Algorithms – The Bellman-Ford algorithm – Optimal Substructure 00:11:00
Algorithms – The Bellman-Ford algorithm – The Basic Algorithm I 00:09:00
Algorithms – The Bellman-Ford algorithm – The Basic Algorithm II 00:11:00
Algorithms – The Bellman-Ford algorithm – Detecting Negative Cycles 00:09:00
Algorithms – The Bellman-Ford algorithm – A Space Optimization 00:13:00
Algorithms – The Bellman-Ford algorithm – Internet Routing I 00:11:00
Algorithms – The Bellman-Ford algorithm – Internet Routing II 00:07:00
Module 12
Algorithms – Problem Definition I 00:07:00
Algorithms – Problem Definition II 00:12:00
Algorithms – The Floyd Warshall Algorithm 00:13:00
Algorithms – A Reweighting Technique 00:14:00
Algorithms – Johnson’s Algorithm I 00:11:00
Algorithms – Johnson’s Algorithm II 00:12:00
Algorithms – Polynomial Time Solvable Problems 00:14:00
Algorithms – Reductions and Completeness 00:14:00
Algoritjms – Definition and Interpretation of NP Completeness I 00:11:00
Algorithms – Definition and Interpretation of NP Completeness II 00:08:00
Algorithms – The P vs NP Question 00:09:00
Algorithms – Algorithmic Approaches to NP Complete Problems 00:13:00
Algorithms – The Vertex Cover Problem 00:09:00
Module 13
Algorithms – Smarter Search for Vertex Cover I 00:10:00
Algorithms – Smarter Search for Vertex Cover II 00:08:00
Algorithms – The Traveling Salesman Problem 00:15:00
Algorithms – A Dynamic Programming Algorithm for TSP 00:12:00
Algorithms – A Greedy Knapsack Heuristic 00:14:00
Algorithms – Analysis of a Greedy Knapsack Heuristic I 00:07:00
Algorithms – Analysis of a Greedy Knapsack Heuristic II 00:10:00
Algorithms – A Dynamic Programming Heuristic for Knapsack 00:12:00
Algorithms – Knapsack via Dynamic Programming, Revisited 00:10:00
Algorithms – Ananysis of Dynamic Programming Heuristic 00:15:00
Module 14
Algorithms – The Maximum Cut Problem I 00:08:00
Algorithms – The Maximum Cut Problem II 00:09:00
Algorithms – Principles of Local Search I 00:09:00
Algorithms – Principles of Local Search II 00:10:00
Algorithms – Principles of Local Search II 00:15:00
Algorithms – Random Walks on a Line 00:16:00
Algorithms – Analysis of Papadimitriou’s Algorithm 00:15:00
Algorithms – Stable Matching Optional 00:15:00
Algorithms – Matchings, Flows, and Braess’s Paradox Optional 00:14:00
Algorithms – Linear Programming and Beyond Optional 00:11:00
Algorithms – Epilogue 00:01:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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