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  • Dynamic Programming

Dynamic Programming Courses

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University of Pennsylvania
Master of Computer and Information TechnologyUniversity of Pennsylvania
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Showing 541 total results for "dynamic programming"

  • Placeholder
    Algorithms
    Stanford University
    SPECIALIZATION
    Rated 4.8 out of five stars. 4778 reviews
    4.8(4,778)
    190k students
    Intermediate LevelIntermediate
  • Placeholder
    Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
    Stanford University
    COURSE
    Rated 4.8 out of five stars. 1091 reviews
    4.8(1,091)
    53k students
    Intermediate LevelIntermediate
  • Placeholder
    Data Structures and Algorithms
    National Research University Higher School of Economics
    SPECIALIZATION
    Rated 4.6 out of five stars. 12528 reviews
    4.6(12,528)
    460k students
    Intermediate LevelIntermediate
  • Placeholder
    Competitive Programmer's Core Skills
    Saint Petersburg State University
    COURSE
    Rated 4.5 out of five stars. 237 reviews
    4.5(237)
    33k students
    Intermediate LevelIntermediate
  • Placeholder
    Reinforcement Learning
    University of Alberta
    SPECIALIZATION
    Rated 4.7 out of five stars. 2088 reviews
    4.7(2,088)
    45k students
    Intermediate LevelIntermediate
  • Placeholder
    Discrete Optimization
    The University of Melbourne
    COURSE
    Rated 4.9 out of five stars. 528 reviews
    4.9(528)
    48k students
    Intermediate LevelIntermediate
  • Placeholder
    程序设计与算法
    Peking University
    SPECIALIZATION
    Rated 4.8 out of five stars. 2543 reviews
    4.8(2,543)
    88k students
    Beginner LevelBeginner
  • Placeholder
    Fundamentals of Reinforcement Learning
    Alberta Machine Intelligence Institute
    COURSE
    Rated 4.8 out of five stars. 1744 reviews
    4.8(1,744)
    43k students
    Intermediate LevelIntermediate
  • Placeholder
    Approximation Algorithms Part I
    École normale supérieure
    COURSE
    Rated 4.8 out of five stars. 133 reviews
    4.8(133)
    23k students
    Mixed LevelMixed
  • Placeholder
    Approximation Algorithms
    EIT Digital
    COURSE
    Rated 4.6 out of five stars. 25 reviews
    4.6(25)
    2.9k students
    Intermediate LevelIntermediate
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    Practical Reinforcement Learning
    National Research University Higher School of Economics
    COURSE
    Rated 4.2 out of five stars. 409 reviews
    4.2(409)
    40k students
    Advanced LevelAdvanced
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    Natural Language Processing with Probabilistic Models
    DeepLearning.AI
    COURSE
    Rated 4.8 out of five stars. 871 reviews
    4.8(871)
    28k students
    Intermediate LevelIntermediate
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    Algorithmic Toolbox
    National Research University Higher School of Economics
    COURSE
    Rated 4.6 out of five stars. 10069 reviews
    4.6(10,069)
    350k students
    Intermediate LevelIntermediate
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    Prediction and Control with Function Approximation
    Alberta Machine Intelligence Institute
    COURSE
    Rated 4.8 out of five stars. 553 reviews
    4.8(553)
    13k students
    Intermediate LevelIntermediate
  • Placeholder
    Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
    Stanford University
    COURSE
    Rated 4.8 out of five stars. 700 reviews
    4.8(700)
    31k students
    Intermediate LevelIntermediate
  • Placeholder
    Bioinformatics: Introduction and Methods 生物信息学: 导论与方法
    Peking University
    COURSE
    Rated 4.4 out of five stars. 178 reviews
    4.4(178)
    22k students
    Mixed LevelMixed
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    Global Health Diplomacy
    The State University of New York
    COURSE
    Rated 4.6 out of five stars. 76 reviews
    4.6(76)
    8.9k students
    Beginner LevelBeginner
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    Advanced Algorithms and Complexity
    National Research University Higher School of Economics
    COURSE
    Rated 4.6 out of five stars. 571 reviews
    4.6(571)
    62k students
    Advanced LevelAdvanced
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    计算导论与C语言基础
    Peking University
    COURSE
    Rated 4.8 out of five stars. 1644 reviews
    4.8(1,644)
    37k students
    Mixed LevelMixed
  • Placeholder
    Delivery Problem
    National Research University Higher School of Economics
    COURSE
    Rated 4.7 out of five stars. 319 reviews
    4.7(319)
    16k students
    Beginner LevelBeginner

Searches related to dynamic programming

dynamic programming languages

greedy algorithms, minimum spanning trees, and dynamic programming

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In summary, here are 10 of our most popular dynamic programming courses

  • Algorithms: Stanford University
  • Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming: Stanford University
  • Data Structures and Algorithms: National Research University Higher School of Economics
  • Competitive Programmer's Core Skills: Saint Petersburg State University
  • Reinforcement Learning: University of Alberta
  • Discrete Optimization: The University of Melbourne
  • 程序设计与算法: Peking University
  • Fundamentals of Reinforcement Learning: Alberta Machine Intelligence Institute
  • Approximation Algorithms Part I: École normale supérieure
  • Approximation Algorithms: EIT Digital

Skills you can learn in Business Strategy

Modeling (25)
Market (economics) (21)
Plan (19)
Planning (16)
Strategic Management (16)
Business Model (13)
Operations Management (13)
Analytics (12)
Evaluation (12)
Project Management (11)
Supply Chain (10)
Decision-making (9)

Frequently Asked Questions about Dynamic Programming

  • Dynamic programming is an algorithmic technique that solves optimization problems by breaking them down into simpler sub-problems. The solutions to these sub-problems are stored along the way, which ensures that each problem is only solved once. Dynamic programming has become an important technique for efficiently solving complex optimization problems in applications such as reinforcement learning for artificial intelligence (AI) and genome sequencing in bioinformatics.

    The advantages of dynamic programming can be understood in relation to other algorithms used to solve optimization problems. Like divide and conquer algorithms, dynamic programming breaks down a larger problem into smaller pieces; however, unlike divide and conquer, it saves solutions along the way so each problem is only solved once, improving the speed of this approach. By contrast, greedy algorithms also solve each problem only once, but unlike dynamic programming, it does not look back to consider all possible solutions, running the risk that the greedy algorithm will settle on a locally optimal solution that is not globally optimal.

    Ultimately, there is no single “silver bullet” algorithm that is best for every application, and different types of problems will require different techniques. However, dynamic programming’s ability to deliver globally optimal solutions with relative efficiency makes it an important part of any programmer’s skill set.

  • Dynamic programming is a valuable career skill for programmers working on complex optimization problems in high-tech fields such as data science, artificial intelligence and machine learning, robotics, and bioinformatics. Computer scientists with the ability to find the right approaches to these high-value problems are highly sought after and compensated accordingly by leading companies in these industries. According to the Bureau of Labor Statistics, computer and information research scientists earned a median annual salary of $122,840 per year in 2019, and these jobs are expected to grow much faster than the average across the rest of the economy.

  • Yes! There are an incredibly wide range of learning opportunities in computer science on Coursera, including courses and Specializations in algorithms and dynamic programming. Coursera lets you learn about dynamic programming remotely from top-ranked universities from around the world such as Stanford University, National Research University Higher School of Economics, and University of Alberta. And, because learners on Coursera pay a significantly lower tuition than on-campus students, you won’t need to use dynamic programming or other algorithmic techniques to determine whether it’s an optimal investment in your career.

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