Ethan Meyers

Associate Professor of Statistics

Cognitive Science

emmCS@hampshire.edu

Personal Website

Mail Code CS

Ethan Meyers

Adele Simmons Hall

413.559.5500

On Leave of Absence Fall 2019, Spring 2020

Ethan Meyers, associate professor of statistics, received his B.A. in computer science from Oberlin College, and earned his Ph.D. in computational neuroscience from the Massachusetts Institute of Technology.

Ethan’s research focuses on creating methods for analyzing high-dimensional neural signals in order to understand the neural processing that underlies object recognition, working memory, and other cognitive processes. His teaching interests span a range of topics, including statistics, machine learning, data science, and computational neuroscience.

Recent and Upcoming Courses

Statistics for Cognitive Science
In this class we will introduce the central concepts in Statistics by running and analyzing data from Cognitive Science experiments. Statistics topics covered in the class include summary statistics, graphical methods, and resampling and parametric inference methods for calculating confidence intervals and conducting hypothesis tests. Students will learn how to use the R programming language to explore statistical concepts and to analyze real data. Assignments will consist of weekly problem sets and a final class project. By the end of the class students will be able to understand the concepts that underlie statistical analyses and will be able to apply statistical methods to data collected from a variety of fields.
Machine Learning
Machine learning is a subfield of artificial intelligence that aims to give computers the ability to make predictions and find relationships in data. The methods used in machine learning blend statistical concepts with ideas from computer science, and are widely used by data scientists to analyze complex datasets, and by artificial intelligence researchers to make intelligent systems. In this class, we will cover the basic concepts in machine learning including regression, supervised learning (classification), unsupervised learning (clustering and dimensionality reduction), cross-validation methods, and model selection. We will use the R and Python programming languages to explore the usefulness of different methods and to analyze real data. The class work will consist of weekly programming problems and a midterm and final project. Prerequisites: prior experience with programming and Statistics. Prerequisite: Prior experience with programming and statistics, either through a class or from other experiences.
Introduction to Statistics
This class is an introduction to descriptive and inferential statistics that are useful for analyzing data from a variety of fields. Topics covered include summary statistics, graphical methods, and resampling and parametric inference methods for calculating confidence intervals and conducting hypothesis tests. Students will learn how to use the R programming language to explore statistical concepts and to analyze real data. Assignments will consist of weekly problem sets and a final class project where students will gain experience analyzing a dataset in more depth. By the end of the class students will be able to understand the concepts that underlie statistical analyses and will be able to apply statistical methods to gain insight into data that they collect.
Introduction to Data Science
Description: With the rise of Internet and other new technologies, large datasets are now available that can give deep insights into questions about science, human nature, and society. However, to extract useful information from this data, powerful data analysis methods are needed. Data Science is a field that addresses this issue by using computational tools to gain insights from large datasets. In this class, students will learn how to apply Data Science methods, and the R programming language, to visualize, manipulate, and make predictions from data. Assignments will consist of weekly readings of data journalism articles, weekly problem sets to practice particular skills, and a midterm and a final project where students will explore a dataset in more depth. By the end of the class students will be able to visualize and analyze data in order to answer a range of interesting questions.