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Stanford University

Algorithms: Design and Analysis, Part 2

Stanford University via Coursera

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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.

About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.

Note: this course is closing on October 10th, 2016, and relaunching as part of a specialization:


Week 1
Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm.

Week 2
Kruskal's MST algorithm and applications to clustering; advanced union-find (optional); Huffman codes.

Week 3
Dynamic programming: introduction, the knapsack problem, sequence alignment, and optimal binary search trees.

Week 4
The Bellman-Ford algorithm; all-pairs shortest paths.

Week 5
NP-complete problems and exact algorithms for them.

Week 6
Approximation and local search algorithms for NP-complete problems; the wider world of algorithms.

Final Exam
Final exam (1 attempt per 24 hours)

Taught by

Tim Roughgarden


4.7 rating, based on 19 Class Central reviews

Start your review of Algorithms: Design and Analysis, Part 2

  • 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 salesma…
  • 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.
  • Profile image for Luiz Cunha
    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
  • 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.
  • Anonymous
    Prof. Roughgarden is unbelievable professional. He does amazing job.
  • Marta
  • Anonymous
  • Rafael Prados
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    Ilya Rudyak
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    Dmitry Kotov

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