Dynamic Programming

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

The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).
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Average: 1 ( 3 votes )

Julia Scientific Programming (Coursera)

This four-module course introduces users to Julia as a first language. Julia is a high-level, high-performance dynamic programming language developed specifically for scientific computing. This language will be particularly useful for applications in physics, chemistry, astronomy, engineering, data science, bioinformatics and many more. [...]
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Average: 1 ( 3 votes )

Comparing Genes, Proteins, and Genomes (Bioinformatics III) (Coursera)

Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or [...]
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Average: 10 ( 4 votes )

算法基础 (Coursera)

Sep 13th 2021
算法基础 (Coursera)
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本课程内容程涵盖枚举、二分、贪心、递归、深度优先搜索、广度优先搜索、动态规划等基本算法。通过大量的高强度的编程训练,提高动手能力,做到能较为熟练、完整、准确地实现自己设计的程序,为进一步学习其他计算机专业课程,或在其他专业领域运用计算机编程解决问题奠定良好的基础。
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Sep 13th 2021
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32.00 EUR/month

Algorithmic Thinking (Part 2) (Coursera)

Sep 13th 2021
Algorithmic Thinking (Part 2) (Coursera)
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Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational [...]
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Average: 1 ( 3 votes )

Data Structures & Algorithms IV: Pattern Matching, Dijkstra’s, MST, and Dynamic Programming Algorithms (edX)

Delve into Pattern Matching algorithms from KMP to Rabin-Karp. Tackle essential algorithms that traverse the graph data structure like Dijkstra’s Shortest Path. Study algorithms that construct a Minimum Spanning Tree (MST) from a graph. Explore Dynamic Programming algorithms. Use the course visualization tool to understand the algorithms and their [...]
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Basics of Mathematical Modeling of Systems (edX)

Basics of mathematical modeling of systems - the course uses examples to teach programming in C#. A prototype of the program is being created for dynamic script execution. We study working with image files and the basics of image processing. An algorithm for dynamic programming in various problems is [...]
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Reinforcement Learning Explained (edX)

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Reinforcement Learning Explained (edX)
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Learn how to frame reinforcement learning problems, tackle classic examples, explore basic algorithms from dynamic programming, temporal difference learning, and progress towards larger state space using function approximation and DQN (Deep Q Network).
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Dynamic Programming: Applications In Machine Learning and Genomics (edX)

Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution. If you look at two genes that serve the same purpose in two different species, how can you rigorously compare these genes in order to see how they have evolved away [...]
10
Average: 10 ( 2 votes )