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There are lots of machines in Machine Learning: Gradient Boosting Machines, Boltzmann Machine, Helmholtz Machine, Support Vector Machine, etc. So, what’s the matter with these **machines**. More importantly, why is it **machine** learning?

In this article, I will introduce the history of the following:

- Machine
- Learning Machines
- Machine Learning
- Machines in Machine Learning

Before we call it computers, people used the term, “Machine”, which was introduced by Alan Turing in his seminal 1936 paper as “UTM” (Universal Turing machine). Back then, “computer” is used to describe a person who did calculations. …

FinTech will further embrace Artificial Intelligence during and after the COVID-19 pandemic. A.I. is the underlying engine supporting all the changes happening. To prepare for the future world, we need better identity verification, need real-time big data, need to be more personalized to operate completely online.

Paper money is almost 1000 years old and the oldest paper money was introduced in Song dynasty China during the 11th century. However, if you go to China, (almost) no one is using cash anymore. You can see QR codes of WeChat Pay or AliPay everywhere.

COVID-19 gives us one more reason to kill…

I was reading about teawares the other day and learned about this interesting cup, aroma cup. It is the first time I’ve heard about it and it’s worth writing a story.

The aroma cup, or sometimes called fragrance cup (闻香杯), is a cup specifically used to small tea. It is the one on the right in the picture above. An aroma cup, a tasting cup, and a saucer together form a set, as seen in the picture. Aroma cup is taller and thinner than the tasting cup on the left. …

Let’s say this university has two classes, with sizes 10 and 50. Each student can only go to one class and all the classes are full.

The school advertises the average class size to be (10+50)/2 =30. However, you don’t believe this and then you randomly selected 6 students from the school, asked them one question: “How big is your class?” One student said 10 and the remaining five said 50. You may then calculate the average class size to be (10+50*5)/6 = 43.33. Apparently, this is larger than what the school advertises but which one is correct?

The python…

Suppose there is a bus, 🚌, it arrives at the bus stop every 10 minutes, how long, on average, do you need to wait for the bus?

Let random variable X represent waiting time. X follows a uniform distribution. In extreme cases, the individual either waits 10 minutes (when arrives, the bus just left) or no time, (just in time to catch the bus).

Hence, we have a = 0, b = 10 for uniform distribution. The mean of the uniform distribution is 1/2*(a+b), we have 5 mins.

However, if, **on average**, the bus arrives at the bus stop every…

Heaping for Yellowing is one of the steps of making yellow tea. Yellow tea is a rare and expensive tea from China. In order to make yellow tea, there are additional steps needed to be performed compared to the making of green tea.

The formal definition of Heaping for Yellowing is *将在杀青或揉捻或烘之后的茶叶，趁热用布或纸将茶叶包裹，放置使其黄变的过程*。*[1]*

Heaping for yellowing is, *using cotton or paper, to wrap tea leaves after fixation or rolling or drying, when they are warm, to make them yellow.*

The process is illustrated in the following pictures:

Let’s explain ROC and AUC first:

**What is ROC?**The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings [1].**What is AUC?**The area under the ROC curve.

First of all, let’s assume that you know what are TP, TN, FP, FN. There are several concepts you need to know:

**Sensitivity (TPR)**: True Positive Rate (same as recall), it measures the proportion of positives that are correctly identified as positive.

*To understand**: If our classifier to detect dog images, Sensitivity is then the number of correctly classified…*

Data Scientist at Affirm | Board Member of cmuteba.com and sfbiclub.com | Previously at CMU and UIUC | https://jiaruixu.com . My views are my own.