Algorithmic Toolbox

Start Date: 12/27/2020

Course Type: Common Course

Course Link:

About Course

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 into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second). Do you have technical problems? Write to us:

Course Syllabus

Welcome to the first module of Data Structures and Algorithms! Here we will provide an overview of where algorithms and data structures are used (hint: everywhere) and walk you through a few sample programming challenges. The programming challenges represent an important (and often the most difficult!) part of this specialization because the only way to fully understand an algorithm is to implement it. Writing correct and efficient programs is hard; please don’t be surprised if they don’t work as you planned—our first programs did not work either! We will help you on your journey through the specialization by showing how to implement your first programming challenges. We will also introduce testing techniques that will help increase your chances of passing assignments on your first attempt. In case your program does not work as intended, we will show how to fix it, even if you don’t yet know which test your implementation is failing on.

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Course Introduction

Algorithmic Toolbox This is the third course in the specialization on Algorithmic topics. We are going to focus on the important areas in computer science that drive the performance of algorithms. This first course builds on the linear algebra and computer science knowledge that is covered in the specialization. We are going to use the pure-instruction-level programming style of functional programming in the programming examples to demonstrate the different algorithms that are built into the language. At the end of this course, you will be able to: - write programs that perform simple tasks efficiently - understand the influence of state on execution - write programs that read large amounts of data - use recursive functions effectively - use foldl to implement folds - understand how to use recursion effectively - write efficient algorithms - apply the foldl framework to implement algorithms Note: This is a functional course, and as such, most of these concepts will require you to use functional programming techniques, libraries, and tools (particularly for algorithms that take a lot of memory).Complexity Reduction Leveraging Recursion Folds Revert and Join Algorithmic Thinking for Game Theory This course makes an attempt to integrate the key theoretical topics in computer science, machine learning and computer graphics, into game theory. The main focus is on systems averse optimization: finding efficient systems over an extended period of time. The course also focuses

Course Tag

Dynamic Programming Debugging Software Testing Algorithms Computer Programming

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