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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

The Introduction to Graduate Algorithms online programme is offered by Udacity and developed by the Georgia Institute of Technology, US. It features rich learning content related to the analysis and design of computer algorithms. The course is free to enrol in and has an approximate timeline of three months. You will get access to video lectures, interactive quizzes and reading material.

Throughout the Introduction to Graduate Algorithms online course by Udacity, you will explore a wide range of graduate-level algorithm concepts. These include computational intractability, Fast Fourier Transform (FFT), dynamic programming, Rivest-Shamir-Adleman (RSA) cryptosystem, minimum spanning tree, Markov chains, Max-SAT approximation, Fibonacci numbers, divide and conquer, randomised algorithms, etc.

Udacity’s Introduction to Graduate Algorithms online training aims to explain the algorithms for fundamental problems and the methods for algorithm design. During this advanced-level programme, you will also study hashing using graph algorithms, linear programming, bloom filters, max-flow algorithm and non-deterministic polynomial-time (NP) completeness.

The Highlights

Advanced-level programme
Online programme
Free course access
3-months timeline
Self-paced learning mode
An offering of Georgia Tech, USA

Programme Offerings

Digital learning platform
Industry expert instructors
Learn-by-doing exercises
Free access
Comprehensive curriculum
3-months training
video lectures
Self-paced learning
Offered by Georgia Tech
Interactive Quizzes

Courses and Certificate Fees

Certificate Availability
no

Introduction to Graduate Algorithms fee structure

Course name	Fee
Introduction to Graduate Algorithms	Free

Eligibility Criteria

The Introduction to Graduate Algorithms course is meant for advanced-level learners. So, you must have an undergraduate programme on the analysis and design of algorithms. Plus, you must be familiar with analysing the asymptotic running time of algorithms.

An undergraduate programme in discrete mathematics is also mandatory to join the Introduction to Graduate Algorithms training. Moreover, you must understand basic graph algorithms such as Dijkstra’s shortest path, BFS, and DFS. You need to be familiar with divide and conquer algorithms and fundamental dynamic programming.

What you will learn

Knowledge of Algorithms

After completing the Introduction to Graduate Algorithms programme, you will understand:

The various techniques of algorithm design
Algorithms for solving fundamental problems
Hashing using linear programming, bloom filters, graph algorithms, etc.
Computational intractability

Who it is for

The Introduction to Graduate Algorithms course is helpful for individuals who want to study advanced computer science or want to work as software engineers.

Admission Details

Step 1 – Go to the link https://www.udacity.com/course/introduction-to-graduate-algorithms--ud401 to open the Introduction to Graduate Algorithms course page.

Step 2 – Tap the ‘Start Free Course’ button to start the registration process.

Step 3 – Sign in through an existing Google or Facebook account. Or, create a new learning account by entering some details and tapping the ‘Sign Up’ button.

That’s all. You will now find yourself on the lessons webpage after enrolling in the Introduction to Graduate Algorithms online course.

Application Details

There is no need to fill and submit an application form for joining the Introduction to Graduate Algorithms training. Visit the Udacity website and sign in/sign up to enrol. Just enter your name, password, and email ID for creating a new account.

The Syllabus

Shortest Path Algorithms
Knapsack, Chain Matrix Multiplication
Fibonacci Numbers, Longest Increasing Subsequence (LIS), Longest Common Subsequence (LCS)

Hashing: Traditional Chain Hashing, Bloom Filters
RSA Cryptosystem: Fermat's Little Theorem, RSA Protocol, Primality Testing
Modular Arithmetic: Fast Modular Exponentiation, Multiplicative Inverses