Seems like a lifetime ago, but I suddenly wanted to pull up some reference books I had used when preparing for INMO-2000 (class 10), INMO-2002 (class 11) and INMO-2003 (class 12)
Resources:
Seems like a lifetime ago, but I suddenly wanted to pull up some reference books I had used when preparing for INMO-2000 (class 10), INMO-2002 (class 11) and INMO-2003 (class 12)
Resources:
Stony Brook [my Alma Mater 🙂 ]
IMSc (Indian Institute of Mathematical Sciences):
MIT
“You are given an n-sided die where side i has probability pi of being rolled. What is the most efficient data structure for simulating rolls of the die?”
A very similar question was posted to me recently :
The approaches above are very cool, and illustrate the use of augmented search trees.
However, it seems there is a better method for this problem – and it has been out there for a while now. This was a fascinating read :
Additional pointers for the alias method:
I came across this interesting set of blog posts by Sergei Feldman on the use of bandit approaches in online recommendation.
In particular, the one I really enjoyed reading was the comparison of the approaches needed to solve the multi armed bandit problem. Need to play around with his code someday
References:
I was solving this math problem which had to do with representing every Natural number as a summation/subtraction of distinct power of 3
Interestingly this led me to this branch of mathematics called ‘Balanced Ternary’. Check it out!
Exploration of this problem gave me interesting insights about base representation of a number, something that I have been keeping in the backburner for a long while now. Finally got a chance to follow up on this.
References:
Problem:
Code:
Do a simple search on Google – ‘how do bandit algorithms work’ ?
Do the results look confusing ? Some links (here1, here2) say they are better than A/B. Then there are other links which say otherwise (here3, here4).
In fact, when one hears about Bandit Problems, there are couple of questions to think about:
Questions:
1.Is it an ‘Experiment Strategy’ ?
2. Is it an ‘ML Algorithm’ ?
3. Where do the algorithms like epsilon-greedy, UCB etc fit into ?
Thoughts:
References:
Euclid’s algorithm for computing the GCD has a lot of interesting extensions for exploration.
References:
Code:
In a previous post , I mentioned about expected value and variance of different distributions.
Taking the same statistical concepts further, we now want to compute confidence intervals for our estimate.
Note:
(https://en.wikipedia.org/wiki/Standard_deviation)
References:
Videos:
Code:
I came across this very interesting talk by some folks at Cornell on Counterfactual Evaluation.
Some thoughts:
References:
Code: