Greedy Algorithms Minimum Spanning Trees and Dynamic Programming

Course Feature

Cost

Free
Provider

Coursera
Certificate

Paid Certification
Language

English
Start Date

No Information
Learners

No Information
Duration

No Information
Instructor

Algorithms Specialization

Add to Favorites

4.8

70,763 Ratings

Unlock the secrets of efficient problem-solving with "Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming." Explore essential topics like scheduling, minimum spanning trees, clustering, and Huffman codes through the lens of greedy algorithms. Dive into dynamic programming and master concepts such as knapsack problems, sequence alignment, and optimal search trees. Gain the tools to optimize resource allocation and solve complex optimization challenges. Delve into this specialized domain and enhance your problem-solving skills. Join now and elevate your algorithmic expertise.

Show All

Go to class

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [July 14th, 2023]

This course focuses on two key topics: greedy algorithms and dynamic programming. Participants will explore various applications of greedy algorithms such as scheduling, minimum spanning trees, clustering, and Huffman codes. Additionally, the course covers dynamic programming concepts including knapsack problems, sequence alignment, and optimal search trees. Through a combination of theory and practical examples, learners will develop a solid understanding of these algorithms and their real-world applications. Join now to enhance your problem-solving skills and master the art of designing efficient algorithms.

Show All

Recommended Courses

USACO Silver C++ Course - Part 1

4.7

Udemy 2,708 learners

Learn More

This USACO Silver C++ Course - Part 1 is perfect for beginner programmers who want to prepare for the USACO Silver competitions. It covers topics such as STL Sort Function and Comparator, Structures and Sorting, and Problem-Solving Practice. The course also includes video solutions to actual Codeforces/USACO problems. Prerequisites include being comfortable with programming in C++. This course is suitable for anyone interested in competitive programming, algorithms, and data structures.

Data Structures And Algorithms In Real World - Part 1

4.5

Udemy 3,202 learners

Learn More

Data Structures and Algorithms are essential topics in computer science. But understanding how they are used in real world software is even more important. This course series will teach you how to use algorithms like Merkle tree, Consistent hashing, and Bloom filter in real world applications. You will learn to build a minimal version of Git from scratch and understand how Bitcoin and DynamoDb use these algorithms. Javascript is the language of choice for this course, but the same concepts can be applied to other languages. Sign up now and learn Data Structures and Algorithms in Real World!

Algorithms in Competitive Programming: Maths Puzzles I

4.3

Udemy 2,143 learners

Learn More

This series of courses on algorithms used in competitive programming is perfect for those looking to improve their problem-solving skills. It focuses on the mathematics behind the algorithms, providing rigorous proofs of correctness and efficiency. Starting with maths puzzles, the course will cover problem description, a naive approach, introducing the algorithm, mathematical proof, a sample problem, a short quiz, and hands-on practice. If you're interested in competitive programming algorithms, this course is a great place to start!

Mathematical Algorithms in Computing using C++

3.8

Udemy 7,807 learners

Learn More

This course is perfect for those who want to learn how to use C++ to solve mathematical problems. It covers the fundamentals of the Math Library black boxes and how developers have tackled the task of making them easy to use. You'll learn how to implement common mathematical algorithms and gain a deeper understanding of computer programming for mathematical problem solving.