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.
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.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
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To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
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 problem
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.
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.