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).
Problem Overview
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Skills You'll Learn
Dynamic Programming, Debugging, Software Testing, Algorithms, Computer Programming
Reviews
Dec 26, 2020
good course, I like the fact you can use a lot of languages for you programming exercises, the content is really helpful, I would like to have more indications from the grading system to save time.
Dec 9, 2020
it's great learning experience, the course has rich and diverse content. I have really improved my algorithmic thinking. the assignment was really challenging but I'm glad to be able to solve them
From the lesson
Algorithmic Warm-up
In this module you will learn that programs based on efficient algorithms can solve the same problem billions of times faster than programs based on naïve algorithms. You will learn how to estimate the running time and memory of an algorithm without even implementing it. Armed with this knowledge, you will be able to compare various algorithms, select the most efficient ones, and finally implement them as our programming challenges!
Taught By
Alexander S. Kulikov
Visiting Professor
Michael Levin
Lecturer
Neil Rhodes
Adjunct Faculty
Pavel Pevzner
Professor
Daniel M Kane
Assistant Professor
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