Algorithms Diploma Level 3 - Edukite

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4/17/2019

Algorithms Diploma Level 3 - Edukite

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Algorithms Diploma Level 3 ( 9 REVIEWS ) 

424 STUDENTS

What is an Algorithm? It is a set of rules that allows you to solve computational problems in an organized …

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LEVEL 3 - DIPLOMA COURSE BADGE COURSE CERTIFICATE

171

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0

NUMBER OF QUIZZES

1 DAY, 12 HOURS

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CURRICULUM

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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 de nite 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 Certi cate at a cost of £49. Having an Of cial Edukite Certi cation is a great way to celebrate and share your success. You can: Add the certi cate to your CV or resume and brighten up your career Show it to prove your success Course Credit: Stanford University

COURSE CURRICULUM https://edukite.org/course/algorithms-diploma-level-3-stanu/

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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

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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

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Algorithms Diploma Level 3 - Edukite

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

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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

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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 De nition 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 De nition

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

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Algorithms Diploma Level 3 - Edukite

Algorithms – Handling Ties

00:07:00

Algorithms – MST Problem De nition

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

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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 De nition

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

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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 De nition

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

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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 De nition I

00:07:00

Algorithms – Problem De nition 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 – De nition and Interpretation of NP Completeness I

00:11:00

Algorithms – De nition 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

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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

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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

Certi cation

00:00:00

COURSE REVIEWS

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