Statistics and Machine Learning: A New Course
The growth in computing power and the availability of data has made statistics and computer science an integral part of modern society and economy. Statistics has important applications in almost every field, from medicine to engineering to the social sciences. As a research tool, statistics is incredibly powerful and allows scholars to better answer questions that range from “what products appeal to consumers of a specific firm?” to “how can we target foreign aid to best tackle poverty?” As a result, on college campuses around the world, statistics has become a highly sought after major. Students in every discipline will often integrate statistics courses into their curriculum. Since statistics is so versatile and tremendously useful, statisticians are the super-stars who are courted by companies in every sector of the economy. Banks require financial data analysts, think tanks and government agencies depend on policy analysts, and even sports teams need research analysts.
We are very excited to introduce a new Horizon Academic course starting in the fall: Statistics and Machine Learning with Georgia Institute of Technology professor, Guillermo Goldzstein. This academic research program for high school students will be a gateway for students to conduct better research and more actively engage with the wealth of statistical literature throughout their academic journeys and future careers.
Computer programming and machine learning are crucial tools for statistical analyses. In this course, students will learn machine learning techniques, the mathematics behind those techniques, and the computer language Python. The goal of the course is to implement those statistical techniques on real data. As a course project, the student will have the option to analyze a real data set or explore a mathematical aspect of machine learning.
Some potential research topics include:
- Email Spam: To design an automatic spam detector that could filter out spam before clogging the users’ mailboxes.
- Prostate Cancer: To predict the chances a patient has or will contract prostate cancer through utilizing a number of clinical measures.
- Speech recognition: To develop a computer program that automatically detects different sounds and/or speech.
- Dynamics of a galaxy: Use data from the motion of stars within the galaxy to understand its dynamics.
- Income from demographics: Understand how different demographic factors are related to the income of an individual.
An Introduction to Professor Goldzstein
Professor Goldsztein is originally from Buenos Aires, Argentina. In 1992 he received his undergraduate degree in mathematics from the University of Buenos Aires and in 1997 a PhD in mathematics from MIT. During the three following years (1997-2000), he was a postdoctoral scholar and lecturer in applied mathematics at CalTech. Since 2000, he has been a faculty member of the School of Mathematics of Georgia Tech, where he is now a full professor.
Why Take This Course?
The world that we live in is awash in data. In daily life and on the news, we constantly encounter data and statistical analyses done by others. The broad goal of the course is to give students knowledge of the mathematical and theoretical foundations of statistics as well as practice with machine learning and computer programming, so they can be more careful and astute consumers of statistics. Through assignments and practice, students will learn to interpret and critically examine data, to come up with research ideas of their own in topics that they are passionate about, and to be a better writer about statistics. Each student will also complete a final paper that is above and beyond the level of most other high school research projects and gap year research projects. Horizon Academic combines structured virtual classroom learning with the flexibility for students to pursue their own interests; and the class offers a collaborative atmosphere where students can learn and discuss with each other under the guidance of seasoned professors and teaching assistants.