In this course you will learn several fundamental principles of advanced algorithm design. You'll learn the greedy algorithm design paradigm, with applications to computing good network backbones (i.e., spanning trees) and good codes for data compression. You'll learn the tricky yet widely applicable dynamic programming algorithm design paradigm, with applications to routing in the Internet and sequencing genome fragments. You’ll learn what NP-completeness and the famous “P vs. NP” problem mean for the algorithm designer. Finally, we’ll study several strategies for dealing with hard (i.e., NP-complete problems), including the design and analysis of heuristics. Learn how shortest-path algorithms from the 1950s (i.e., pre-ARPANET!) govern the way that your Internet traffic gets routed today; why efficient algorithms are fundamental to modern genomics; and how to make a million bucks in prize money by “just” solving a math problem!
Weeks 1 and 2: The greedy algorithm design paradigm. Applications to optimal caching and scheduling. Minimum spanning trees and applications to clustering. The union-find data structure. Optimal data compression.
Weeks 3 and 4: The dynamic programming design paradigm. Applications to the knapsack problem, sequence alignment, shortest-path routing, and optimal search trees.
Weeks 5 and 6: Intractable problems and what to do about them. NP-completeness and the P vs. NP question. Solvable special cases. Heuristics with provable performance guarantees. Local search. Exponential-time algorithms that beat brute-force search.
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.
How do I register?
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.