#229 overall
Analytic Combinatorics
Coursera
2 recommenders · 2 threads · recommended in 2 years, 2013–2024 · score 1.8
A Coursera course by Robert Sedgewick on analytic combinatorics, developing ideas from his book on counting problems and algorithm analysis using generating functions and related mathematics.
Commenters describe it as an incredible topic that pulls undergraduate mathematics into counting problems and algorithm analysis, and one calls it among the most rewarding courses they took.
Mathematicsadvancedfreevideo lectures#analytic combinatorics#generating functions#algorithm analysis#sedgewick#counting
Recommendations by year
20062026
Robert Sedgewick also has created a Coursera course for analytic combinatorics where he develops some of the ideas in his book.
https://www.coursera.org/course/ac
The topic is incredible. Basically all the math you learn in Undergraduate mathematics gets pulled in to solve counting problems and perform algorithm analysis. It's inspiring. :)
I used to take a lot of courses on cousera and EdX. I still take some here and there, but not as many as before. Some of the courses are amazing and unbelievably rewarding, like Daphne Koller's Probabilistic Graphical Models, Robert Sedgewick's Analytical Combinatorics, and Gerald Sussman's course on system optimizations. I'm very grateful for such learning opportunities. Unfortunately over time, I also found that these courses had diminishing returns for the following reasons:
- Due to the nature of MOOC, the assignments are largely either multiple-choice questions or programming assignments that merely asked students to fill in some blanks in some functions (there are a few exceptions of course). What a descent US university does really well is challenging students with tough yet insightful and inspiring assignments. That's how students learn deeply and retain the knowledge, at least for me. Merely listening to lectures and ticking off a few ABCDs hardly helps real learning.
- Lack of feedback. A university course assigns TAs, gives tutorials and office hours, grades assignments with detailed feedbacks, and it is so much easier to form study groups and have high bandwidth discussions. MOOCs try their best to offer such help, but they don't work as well or at least not as conveniently.
- Many courses are watered down. For instance, Andrew Ng's ML course on Coursera is far less rigorous than that (229? I forgot) offered in Stanford? The course is great for students to gain some intuition, but I'm not sure if it's good enough for one to build solid ML foundations.