Algorithmic Toolbox
University of California, San Diego and Higher School of Economics via Coursera
- Provider Coursera
- Cost Free Online Course (Audit)
- Session In progress
- Language English
- Certificate Paid Certificate Available
- Duration 6 weeks long
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Overview
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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.
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!
Greedy Algorithms
-In this module you will learn about seemingly naïve yet powerful class of algorithms called greedy algorithms. After you will learn the key idea behind the greedy algorithms, you may feel that they represent the algorithmic Swiss army knife that can be applied to solve nearly all programming challenges in this course. But be warned: with a few exceptions that we will cover, this intuitive idea rarely works in practice! For this reason, it is important to prove that a greedy algorithm always produces an optimal solution before using this algorithm. In the end of this module, we will test your intuition and taste for greedy algorithms by offering several programming challenges.
Divide-and-Conquer
-In this module you will learn about a powerful algorithmic technique called Divide and Conquer. Based on this technique, you will see how to search huge databases millions of times faster than using naïve linear search. You will even learn that the standard way to multiply numbers (that you learned in the grade school) is far from the being the fastest! We will then apply the divide-and-conquer technique to design two efficient algorithms (merge sort and quick sort) for sorting huge lists, a problem that finds many applications in practice. Finally, we will show that these two algorithms are optimal, that is, no algorithm can sort faster!
Dynamic Programming 1
-In this final module of the course you will learn about the powerful algorithmic technique for solving many optimization problems called Dynamic Programming. It turned out that dynamic programming can solve many problems that evade all attempts to solve them using greedy or divide-and-conquer strategy. There are countless applications of dynamic programming in practice: from maximizing the advertisement revenue of a TV station, to search for similar Internet pages, to gene finding (the problem where biologists need to find the minimum number of mutations to transform one gene into another). You will learn how the same idea helps to automatically make spelling corrections and to show the differences between two versions of the same text.
Dynamic Programming 2
-In this module, we continue practicing implementing dynamic programming solutions.
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Reviews for Coursera's Algorithmic Toolbox Based on 23 reviews
- 5 stars 48%
- 4 stars 9%
- 3 stars 17%
- 2 star 4%
- 1 stars 22%
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- The course supports programming assignments in multiple languages: C, C++, Python, Java. You can implement your algorithms in all 4 languages and learn all of them. They have automatic grader for all 4 languages.
- Your algorithms need to be optimized to pass the assignments, not just creating output correctly. The grader was designed to test you on 3 criteria: Correct Answer, Time Limit and Memory Limit. This course really forces learner…
this review helpful
Strongly recommend!!! Another amazing MOOC from UCSD!!!
this review helpful
In addition, the course is highly adjustable for each stude…
All of them do at least a passable job(all of them are top notch researchers by the way). The more engaging one, in my opinion, is Dr. Pavel Pevzner, he is very energetic and presents some interesting examples in the bioinformatics context, it's a shame that he teaches very few modules. The least engaging for me was also the one that presents the majority of the modules: Alexander S. Kulikov. The quality of his modules varies a lot. At times he seems…
this review helpful
Algorithms is not easy to teach. Smart people aren't necessarily good teachers. Only the visiting professor from Russia, Alexander Kulikov is super clear in his thinking and conveys the right way to traverse these complex concepts. Every word he says is relevant and is necessary and meaningful.
Other profs, explain simple or unnecessarily things for a long time and they really need to focus on how to convey the complex concepts in a better way to improve this course. The programming exercises are good, though they could be even better.
Please add some mini project to the course. thank you
i have to read a lot and search online for other videos and tutorials to understand whats going on
not recommended
The accents are unintelligible. The explanations are poor. The course does not have any meat and bones. In the fifth week, we have barely progressed to dynamic programming.
I can't give lower than 5 stars hence the one star.
Save your time and money and take the Stanford and Princeton courses.
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