Dynamic Programming

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Introduction to Computational Thinking and Data Science (edX)

Oct 18th 2023
Introduction to Computational Thinking and Data Science (edX)
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This course is an introduction to using computation to understand real-world phenomena. This course will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving. This course is aimed at students [...]

Decision Making and Reinforcement Learning (Coursera)

Oct 2nd 2023
Decision Making and Reinforcement Learning (Coursera)
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This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate [...]

Dynamic Programming, Greedy Algorithms (Coursera)

Oct 2nd 2023
Dynamic Programming, Greedy Algorithms (Coursera)
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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 [...]

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 [...]

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).

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, [...]

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 [...]

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).

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. [...]