Dynamic Programming

Sort options

Dynamic Programming, Greedy Algorithms (Coursera)

Oct 18th 2021
Dynamic Programming, Greedy Algorithms (Coursera)
Course Auditing
Categories
Effort
Languages
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 [...]
0
No votes yet

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 [...]
0
No votes yet

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 [...]
0
No votes yet

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).
5
Average: 5 ( 4 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. [...]
1
Average: 1 ( 4 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 [...]
7
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 [...]
7
Average: 7 ( 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 [...]
10
Average: 10 ( 4 votes )

Introduction to Computational Thinking and Data Science (edX)

Oct 13th 2021
Introduction to Computational Thinking and Data Science (edX)
Course Auditing
Categories
Effort
Languages
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 [...]
9
Average: 9 ( 4 votes )

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 [...]
3
Average: 3 ( 4 votes )