人工智能与数据科学面试题了解一下

文章内容片段来源于Vimarsh Karbhari发布在medium.com的一篇文章“Google AI Interview Questions— Acing the AI Interview”

  1. What is the derivative of 1/x

求1/x的导数

y = 1/x

y’ = -1\x^2

  1. Draw the curve log(x+10)

画出log(x+10)的曲线

  1. How to design a customer satisfaction survey?

如何设计客户满意度调查

客户满意:一个人通过对一种事物的感知效果与他/她的期望相比所形成的愉悦或失望的感觉状态。主要是一个企业服务和客户需求的权衡博弈关系。看个公式:

c = b/a

式中:

c—–客户满意度
b—–客户对产品或服务所感知的实际体验
a—–客户对产品或服务的期望值

客户满意度的指标可以参考美国学者杜卡的观点:

  • 与产品有关的指标。如产品的质量、价格、设计、包装
  • 与服务有关的指标。如保修期、送货服务、售后服务
  • 与购买相关的指标。如购买过程中客户与企业之间的互动。

待更新

 

  1. Tossing a coin ten times resulted in 8 heads and 2 tails. How would you analyze whether a coin is fair? What is the p-value?

掷一个硬币10次,8次正面,2次反面。你如何分析硬币的重量是否分布均匀,p值是多少

  1. You have 10 coins. You toss each coin 10 times (100 tosses in total) and observe results. Would you modify your approach to the the way you test the fairness of coins?

你有10个硬币,每个硬币抛掷10次(共抛100次),并观察结果。你会修改检查硬币分布是否均匀的方法吗

  1. Explain a probability distribution that is not normal and how to apply that?

解释一个非正态的概率分布以及如何应用

  1. Why use feature selection? If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression? What are the confidence intervals of the coefficients?

为什么要用特征选择?如果两个预测值高度相关,对逻辑回归的系数有什么影响,系数的置信区间呢

  1. K- mean and Gaussian mixture model: what is the difference between K-means and EM?

K-mean聚类算法与高斯混合模型:K-means聚类与EM最大期望算法的区别是什么

  1. When using Gaussian mixture model, how do you know it is applicable? (Normal distribution)

运用高斯混合模型时,你怎么判断它是否适用(正态分布)

  1. If the labels are known in the clustering project, how to how to evaluate the performance of the model?

在聚类中已知标签,如何评价模型的表现

  1. You have a google app and you make a change. How do you test if a metric has increased or not?

你有一个google app,并对其进行了修改,如何测试改进

  1. Describe the process of data analysis?

描述数据分析过程

  1. Why not logistic regression, why GBM?

为什么不用逻辑回归,而用GBM算法

  1. Derive the equations for GMM.

推导GMM公式

  1. How would you measure how much users liked videos?

如何衡量用户对视频的喜爱程度

  1. Simulate a bivariate normal

模拟一个二元正态分布

  1. Derive variance of a distribution

推导一个分布的方差

  1. How many people apply to Google per year?

每年有多少人申请谷歌的工作

  1. How do you build estimators for medians?

如何估计中位数

  1. If each of the two coefficient estimates in a regression model is statistically significant, do you expect the test of both together is still significant?

对一个回归模型中的两个系数分别做估计时是统计显著的,那么两个一起检验是否仍然显著

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