Dynamic Programming

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Dynamic Programming, Greedy Algorithms (Coursera)

Jan 24th 2022
Dynamic Programming, Greedy Algorithms (Coursera)
Course Auditing
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data [...]
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Competitive Programmer's Core Skills (Coursera)

During the course, you’ll learn everything needed to participate in real competitions — that’s the main goal. Along the way you’ll also gain useful skills for which competitive programmers are so highly valued by employers: ability to write efficient, reliable, and compact code, manage your time well when it’s [...]
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Fundamentals of Reinforcement Learning (Coursera)

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make [...]
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Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming (Coursera)

The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
Average: 5 ( 4 votes )

Approximation Algorithms Part I (Coursera)

How efficiently can you pack objects into a minimum number of boxes? How well can you cluster nodes so as to cheaply separate a network into components around a few centers? These are examples of NP-hard combinatorial optimization problems. It is most likely impossible to solve such problems efficiently, [...]
Average: 1 ( 3 votes )

Advanced Algorithms and Complexity (Coursera)

You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal [...]
Average: 7 ( 4 votes )

Algorithmic Toolbox (Coursera)

The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem [...]
Average: 7 ( 3 votes )

Competitive Programming for Beginners (Coursera)

This online course will help you to join the world of competitive programming and even become worldwide competitions participant! The course includes theoretical and practical aspects that are necessary to solve problems of any difficulties. After this course, you will learn what types of problems you will have to [...]
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Practical Reinforcement Learning (Coursera)

Welcome to the Reinforcement Learning course. Here you will find out about: foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc.--- with math & batteries included; using deep neural networks for RL tasks --- also known as "the hype train"; state of the art RL algorithms--- and how [...]
Average: 3 ( 4 votes )

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).
Average: 1 ( 4 votes )