COVID-19 Updates: Fall Semester

Alexander Lyford

Assistant Professor of Mathematics

 
 work(802) 443-5564
 Fall 2020: Monday 1:30-2:30 PM, Tuesday 3:30-5:00 PM, Wednesday 4:30-Infinity, and by appointment.
 Warner Hall 310

Alex Lyford is an Assistant Professor of Mathematics, and he has been at Middlebury College since 2017. He recieved a Ph.D. in Statistics from the University of Georgia, and his research areas of interest are machine learning, text analysis, statistics education, and math games. Alex's hobbies include sports, hiking, and playing board games. Alex also hosts Board Game Night in the Math department once a month on Mondays.

Students interested in doing research with Alex should stop by his office any time or contact him via email.

 

Courses

Course List: 

Courses offered in the past four years.
indicates offered in the current term
indicates offered in the upcoming term[s]

FYSE 1216 - Mathematics of Board Games      

Mathematics of Board Games
People have been playing games since as early as 2000 B.C. Since then, avid players have devised strategies to maximize their chances of winning. In this course we will dissect a variety of modern board games and analyze various strategies for each game using mathematics, computers, and intuition. We will further discuss whether an optimal strategy exists for each game and propose modifications to existing rules and scoring schemes. The course will culminate with a project to construct a board game. All are welcome regardless of mathematical background. 3 hrs. sem CW DED

Fall 2020

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MATH 0116 - Intro to Statistical Science      

Introduction to Statistical Science
A practical introduction to statistical methods and the examination of data sets. Computer software will play a central role in analyzing a variety of real data sets from the natural and social sciences. Topics include descriptive statistics, elementary distributions for data, hypothesis tests, confidence intervals, correlation, regression, contingency tables, and analysis of variance. The course has no formal mathematics prerequisite, and is especially suited to students in the physical, social, environmental, and life sciences who seek an applied orientation to data analysis. (Credit is not given for MATH 0116 if the student has taken ECON 0210 or PSYC 0201 previously or concurrently.) 3 hrs. lect./1 hr. computer lab. DED

Fall 2017, Spring 2018, Fall 2018, Spring 2019

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MATH 0216 - Introduction to Data Science      

Introduction to Data Science
In this course students will gain exposure to the entire data science pipeline: forming a statistical question, collecting and cleaning data sets, performing exploratory data analyses, identifying appropriate statistical techniques, and communicating the results, all the while leaning heavily on open source computational tools, in particular the R statistical software language. We will focus on analyzing real, messy, and large data sets, requiring the use of advanced data manipulation/wrangling and data visualization packages. Students will be required to bring their own laptops as many lectures will involve in-class computational activities. 3 hrs lect./disc. DED

Fall 2017, Spring 2018, Fall 2018, Spring 2019, Fall 2019, Fall 2020

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MATH 0218 - Statistical Learning      

Statistical Learning
This course is an introduction to modern statistical, machine learning, and computational methods to analyze large and complex data sets that arise in a variety of fields, from biology to economics to astrophysics. The theoretical underpinnings of the most important modeling and predictive methods will be covered, including regression, classification, clustering, resampling, and tree-based methods. Student work will involve implementation of these concepts using open-source computational tools. (MATH 0216) 3 hrs. lect./disc. DED

Spring 2020

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MATH 0311 - Statistics      

Statistics
An introduction to the mathematical methods and applications of statistical inference. Topics will include: survey sampling, parametric and nonparametric problems, estimation, efficiency and the Neyman-Pearsons lemma. Classical tests within the normal theory such as F-test, t-test, and chi-square test will also be considered. Methods of linear least squares are used for the study of analysis of variance and regression. There will be some emphasis on applications to other disciplines. (MATH 0310) 3 hrs. lect./disc. DED

Spring 2018, Spring 2020

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MATH 0500 - Advanced Study      

Advanced Study
Individual study for qualified students in more advanced topics in algebra, number theory, real or complex analysis, topology. Particularly suited for those who enter with advanced standing. (Approval required) 3 hrs. lect./disc.

Spring 2018, Fall 2018, Winter 2019, Spring 2019, Fall 2019, Winter 2020, Spring 2020, Fall 2020, Winter 2021, Spring 2021

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Department of Mathematics

Warner Hall
303 College Street
Middlebury College
Middlebury, VT 05753