Algorithmic Toolbox
University of California, San Diego and Higher School of Economics via Coursera
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Overview
Class Central Tips
This online 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).
Syllabus
- Programming Challenges
- 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.
Taught by
Michael Levin, Daniel M Kane, Alexander S. Kulikov, Pavel Pevzner and Neil Rhodes
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Reviews
3.6 rating, based on 23 Class Central reviews
4.6 rating at Coursera based on 12417 ratings
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One of the best Computer Science algorithm courses (and hopefully, entire specialization) on Coursera's new platform. Here's why: - The course supports programming assignments in multiple languages: C, C++, Python, Java. You can implement your algor…
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I recommend this course, but it is not without its flaws. It has a lot of instructors and thus the quality of lectures varies a bit. All of them do at least a passable job(all of them are top notch researchers by the way). The more engaging one, in…
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Awesome course! Strongly recommend for people who want to learn algorithms from the ground up. However, basic programming will not be taught. So if you want to fare easily in this course, you should have basic ideas of programming in C++ Python Java. All the people in the discussion forums all have programming experience, so if you're completely new please don't enroll because if you ask basic questions, it's hard to get an answer because everyone is busy discussing about algorithm efficiency, memory usage, etc.
Strongly recommend!!! Another amazing MOOC from UCSD!!! -
I am unhappily surprised by the fact that some people rate this course low because it is allegedly poor explained. In my opinion, it is probably the clearest, best explained and best structured course among all algorithmic courses I've ever taken. L…
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[+] Great lectures! [+] Constant in-video quizzes to check understanding of the current lecture [-] Really short (typically 3-5 multiple choice questions) and easy weekly quizzes, would love to have a bit more challenging questions in there, or just more examples of different problems that are solved with recently learned algorithm. But you can cover this by doing exercises from recommended textbook(s), so not a big problem [+] Nice and relevant, not too hard programming assignments, with one or two optional advanced problems each week
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I have completed the first three classes of this series.
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. -
The course was okay, but it is not an introductory course, since it presupposes programming and mathematical knowledge. It gives an insight into computational complexity, but I believe it should be more detailed for a specialization like this. Instructors were okay, and the assignments were a bit difficult, but they were really well documented.
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I am at the last week of the course. Only problem is that lectures are not self sufficient for the course as you have to learn from other sources too. Accent of the two professors are difficult to understand. Although Programming assignments are challenging and fun to solve. Challenges are the parts from which you can learn a lot.
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Yes, the course really helped me a lot because i'm a student from non-computer science background and this course has a lot of good content and helped me to think about the problem more effectively.
Please add some mini project to the course. thank you -
I would not recommend this course to anyone. I found the videos to be poorly put together and not very educational. Wasting 2 video lectures talking about pointless stuff. The instructor didn't explain concepts well or spend the time to teach the important principles. I didn't like it and stopped with the course. Trying to find a better one.
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Taking this class right now,it does have good materials, but there is pretty much no support(QA) from faculty or mentor, when you get stuck in a problem your only hope is someone might have ask a slimier question online - it could take you hours to figure out something... Not recommend.
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I've been programming professionally for 15 years, but I had a significant gap in my knowledge when it came to data structures and algorithms. This course helped me complete my skill set and regain a competitive advantage in the field. Thanks, instructors!
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This class is awesome with problem sets like you would expect from a top university. You already need to know how to code and to handle input and output. If you are stuck with that, look at the tutorials on hackerrank.
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explanations are very poor
i have to read a lot and search online for other videos and tutorials to understand whats going on
not recommended -
There is a reason why this course is not free and you have to pay upfront to take it. Because, if it was free, most people would drop it after the second week.
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. -
The level of difficulty and level of explanation vary greatly throughout the course. I really hate that I paid for this course, and would not recommend it to anyone. I have to sit it out, but it is boring and ill-explained.
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