Posted in Algorithm, Machine Learning, Theory

ML Recap Slides sharing

Few friends with me did some works together since last October. All of us were looking for jobs in machine learning or deep learning. We all agreed that we need to review some interesting algorithms together. We had a draft of machine learning algorithms (part 1) during this new year:

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Click here for a full version: mlrecap.

Also, we are working on part 2; there are some advanced algorithms which you can see from our outline. It is expected to finish around this June.

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These slides are suitable for people to review old things. Some details are not included, so do not suggest readers learn some concepts from our slides. If you find mistakes, please leave comments. If you are interested in some particular algorithms, leave comments and we will consider updating our part 2 outline.

Posted in Machine Learning, Statitics, Theory

Is your model good enough? Evaluation metrics in Classification and Regression

Was working on my research with sklearn, but realized that choosing the right evaluation metrics was always a problem to me. If someone asks me ,”does your model performs well?” The first thing in my mind is “accuracy”. Besides the accuracy, there are a lot, depending on your own problem. Continue reading “Is your model good enough? Evaluation metrics in Classification and Regression”

Posted in Algorithm, Machine Learning, Statitics

Lucky or not: Monte Carlo Method

AlphaGo!

http://c.brightcove.com/services/viewer/federated_f9?isVid=1&isUI=1

When you play any games, probably you have strategies or experiences. But you could not deny that some times you need luck, which data scientists would say a “random choice”. Monte Carlo Method provides only an approximate optimizer, thus giving you the luck to win a game.
Continue reading “Lucky or not: Monte Carlo Method”