NP-completeness Courses and Certifications

Algorithms: Design and Analysis, Part 2

Explore advanced algorithm design techniques like greedy algorithms, dynamic programming, and NP-completeness. Master fundamental concepts through problem sets and programming assignments.

• edX
• 6 weeks, 2-4 hours a week
• On-Demand
• Free Online Course (Audit)

Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

810 ratings at Coursera

Explore advanced graph algorithms, NP-completeness, and strategies for tackling computationally intractable problems. Learn Bellman-Ford, Floyd-Warshall, and heuristic approaches for complex algorithmic challenges.

• Coursera
• 13 hours 55 minutes
• On-Demand
• Free Online Course (Audit)

Introduction to Graduate Algorithms

Learn advanced techniques for designing algorithms and apply them to hard computational problems.

• Udacity
• 12 weeks
• On-Demand
• Free Online Course

Intro to Theoretical Computer Science

2 reviews

Learn the basic concepts in theoretical computer science. Discover what they imply for solving tough computational challenges.

• Udacity
• 8 weeks
• On-Demand
• Free Online Course

NPTEL;  IIT Bombay

Design & Analysis of Algorithms

1 review

Comprehensive exploration of algorithm design techniques, analysis frameworks, and problem-solving strategies, covering divide-and-conquer, greedy algorithms, dynamic programming, and NP-completeness.

• YouTube
• 1 day 6 hours
• On-Demand
• Free Online Course

IIT Kharagpur;  NPTEL

Approximation Algorithm

Explore efficient algorithms for NP-complete problems, covering vertex cover, set cover, knapsack, linear programming, rounding techniques, primal-dual method, and semidefinite programming.

• Swayam
• 12 weeks
• On-Demand
• Free Online Course

IIT Hyderabad;  NPTEL

Computational Complexity

Explore computational complexity theory, models of computation, and complexity classes. Learn about time, space, randomness, and other aspects of computation in this advanced computer science course.

• Swayam
• 12 weeks
• On-Demand
• Free Online Course

Simons Institute

Oral History - Russell Impagliazzo in Conversation with Dick Karp

Explore key developments in theoretical computer science through an engaging conversation between Russell Impagliazzo and Dick Karp, covering topics from NP-completeness to computational biology.

• YouTube
• 1 hour 24 minutes
• On-Demand
• Free Online Course

Ryan O'Donnell

Quantum Complexity - Quantum Computation at CMU

Explore quantum complexity, error probabilities, and complexity classes in this advanced lecture on quantum computation and information theory.

• YouTube
• 1 hour 23 minutes
• On-Demand
• Free Online Course

International Centre for Theoretical Sciences

Computational Complexity in Theory and in Practice by Richard M. Karp

Explore the contrast between theoretical and practical approaches to algorithm efficiency, covering complexity classes, NP-completeness, approximation algorithms, and real-world applications.

• YouTube
• 1 hour 17 minutes
• On-Demand
• Free Online Course

Simons Institute

Quantum Constraint Satisfaction - Richard M. Karp Distinguished Lecture

Explore quantum Hamiltonian complexity, comparing it to classical constraint satisfaction. Learn about QMA-completeness, search-to-decision, dichotomy theorems, and coping with QMA-hardness in quantum applications.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Online Course

算法设计与分析(高级) | Advanced Design and Analysis of Algorithms

算法设计与分析(高级)”是在掌握了算法基本设计技术的基础上，进一步提高的算法课程。本课程重点介绍线性规划、网络流算法、计算复杂性理论、近似算法、随机算法及其应用。

• edX
• 10 weeks, 2-3 hours a week
• On-Demand
• Free Online Course (Audit)

Algorithms: Design and Analysis, Part 2

19 reviews

Explore advanced algorithm design techniques including greedy algorithms, dynamic programming, and NP-completeness. Gain practical skills for solving complex computational problems efficiently.

• Coursera
• 7 weeks, 6-10 hours a week
• Finished
• Free Online Course (Audit)

Algorithm Design and Analysis

Learn about the core principles of computer science: algorithmic thinking and computational problem solving.

• edX
• 4 weeks, 6-8 hours a week
• Finished
• Free Online Course (Audit)

Dynamic Programming, Greedy Algorithms, and Intractability

Master algorithm design techniques and explore advanced data structures, focusing on dynamic programming, greedy algorithms, and intractability concepts for efficient problem-solving.

• Coursera
• 9 hours
• Finished
• Free Online Course (Audit)