#8 Math in America

Consider about post-graduate develop in careers. If you hope to stay in the U.S to study further, to be a graduate student, or to establish your own business, major in math will always be a good choice. Mathematics study is sophisticated and comprehensive here. Moreover, because it can be divided into traditional, educational, applied and statistical track, fields of study are diversified enough to select. According to my primary survey among international students at the math department in University of Maryland, College Park, here are two of the most popular derivatives in practical math:

Machine Learning

Machine learning nowadays relates closely with artificial intelligence. It is to research on whether computers can learn from data themselves. Here are some typical application (“Machine Learning: What it is and why it matters”):

  • The heavily hyped, self-driving Google car? The essence of machine learning.
  • Online recommendation offers such as those from Amazon and Netflix? Machine
  • learning applications for everyday life.
  • Knowing what customers are saying about you on Twitter? Machine learning
  • combined with linguistic rule creation.
  • Fraud detection? One of the more obvious, important uses in our world today.

Thanks to the fleeting develop in technology today, machine learning has been given novel definition and has been more popular than ever before. Today, it can be applied in finance, government services, transportation, oil and gas, and etc. However, back to the core, it is about the data mining and Bayesian analysis. It is also currently a major research at the Norbert-Wiener center, modeling research center at the department of mathematics in University of Maryland.


Times Series

There are multiple definitions on time series. It can be regarded as a collection of quantitative observations that are evenly spaced in time and measured successively (Anne Senter). Examples of time series include (Anne Senter):

  • the continuous monitoring of a person’s heart rate
  • hourly readings of air temperature
  • daily closing price of a company stock
  • monthly rainfall data, and yearly sales figures.

As application listed above, it is widely applied to continuous variables in health care, medical science, environmental science, finance, and etc.

Goals of time series analysis (Anne Senter):

  • Descriptive: Identify patterns in correlated data—trends and seasonal variation
  • Explanation: understanding and modeling the data
  • Forecasting: prediction of short-term trends from previous patterns
  • Intervention analysis: how does a single event change the time series?
  • Quality control: deviations of a specified size indicate a problem


word count: 404

Work Cited

“Machine Learning: What it is and why it matters”. SAS. www.sas.com/en_us/insights/analytics/machine-learning.html#. Accessed 18 November 2016

Anne Senter. “Time Series Analysis”. sfsu.edu. userwww.sfsu.edu/efc/classes/biol710/timeseries/timeseries1.htm. Accessed 18 November 2016


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