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Algorithms: Design and Analysis, Part 2

Algorithms: Design and Analysis, Part 2

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Product is rated as #29 in category Data Structures and Algorithms

Welcome to the self paced course, Algorithms: Design and Analysis, Part 2! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This course is an introduction to algorithms for learners with at least a little programming experience. The course is rigorous but emphasizes the big picture and conceptual understanding over low–level implementation and mathematical details. After completing this course, you will have a greater mastery of algorithms than almost anyone without a graduate degree in the subject. Specific topics in Part 2 include: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes), dynamic programming (knapsack, sequence alignment, optimal search trees, shortest paths), NP–completeness and what it means for the algorithm designer, analysis of heuristics, local search. Learners will practice and master the fundamentals of algorithms through several types of assessments. There are 6 multiple–choice problem sets to test your understanding of the most important concepts. There are also 6 programming assignments, where you implement one of the algorithms covered in lecture in a programming language of your choosing. The course concludes with a multiple–choice final. There are no assignment due dates and you can work through the course materials and assignments at …

Instructor Details

Tim Roughgarden is a Professor of Computer Science and (by courtesy) Management Science and Engineering at Stanford University. He received a BS in Applied Mathematics from Stanford in 1997, and a PhD in Computer Science from Cornell in 2002. His research interests include the many connections between computer science and economics, as well as the design, analysis, applications, and limitations of algorithms. For his research, he has been awarded the ACM Grace Murray Hopper Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), the Kalai Prize in Computer Science and Game Theory, the Shapley Lecturership of the Game Theory Society, the Social Choice and Welfare Prize, INFORM’s Optimization Prize for Young Researchers, the Mathematical Programming Society’s Tucker Prize, the EATCS-SIGACT Godel Prize, and a Guggenheim Fellowship.

Specification: Algorithms: Design and Analysis, Part 2

Duration 18 hours
Year 2020
Level Intermediate
Certificate Yes
Quizzes No

5 reviews for Algorithms: Design and Analysis, Part 2

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    Anonymous

    I took similar courses in university for my Master of Science. But it was many years ago, few graph algorithms were completely new to me. Quite challenging assignments will take a bit of your free time. Overall, it was nice to feel like a student again.

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    Anonymous

    Very good course, just like part I. This course seemed much harder than part I, but it was still doable. Prof Roughgarden does a very good job teaching the material.

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    Anonymous

    Prof. Roughgarden is unbelievable professional. He does amazing job.

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    Life is Study

    Part 2 picks up where part 1 left off, so completing part 1 fist is highly recommended. Major topics covered in part 2 include minimum spanning tree algorithms, the knapsack problem, dynamic programming, shortest path problems, the traveling salesman problem, P vs. NP and NP completeness and heuristics for hard problems.

    Part 2 is considerably harder than part 1 and the algorithms you write for homework need to be implemented well to get answers in a reasonable amount of time and without exceeding your system’s memory. It is possible to complete the class using a high level language like Python, but you’ll probably have to spend a bit more time tweaking your code to get solutions in a reasonable amount of time. In the end I was able to solve all the programming assignments, but this one is definitely not for the feint of heart. I’d like to post some of the algorithms I wrote for the course, but it is against the Coursera honor code to make solutions available to others.

    My biggest gripe with the class is that the coverage of the P vs. NP question and NP completeness is brief, so students don’t gain a deep understand of what P vs. NP and NP completeness really mean. The introduction to theoretical computer science by Udacity provides a much more through overview of that particular topic. That said, the overall quality of the instruction and assignments in this course are top notch and I’d highly recommend parts 1 and 2 to anyone looking for a solid foundation in algorithm design and analysis.

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    Luiz Cunha

    In my top 3 MOOC.

    Great content and delivery by Prof. Roughgarden on a rather dry topic.

    The videos are great quality.

    The assignments are very challenging but interesting.

    I just wished this MOOC would be longer

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