Adam Kashlak, PhD

Area of Study / Keywords

Functional Data Stochastic Processes Mathematical Statistics Randomization Testing

About

Research

Teaching

Courses I have taught:  STAT 378, 413, 479, 568, 571

STAT 378 - Applied Regression Analysis

Simple linear regression analysis, inference on regression parameters, residual analysis, prediction intervals, weighted least squares. Multiple regression analysis, inference about regression parameters, multicollinearity and its effects, indicator variables, selection of independent variables. Non-linear regression. Prerequisite: One of STAT 266 or STAT 276, or STAT 235 with consent of the Department.

STAT 413 - Computing for Data Science

Survey of contemporary languages/environments suitable for algorithms of Statistics and Data Science. Introduction to Monte Carlo methods, random number generation and numerical integration in statistical context and optimization for both smooth and constrained alternatives, tailored to specific applications in statistics and machine learning. Prerequisites: One of STAT 265 or STAT 281, or consent of the Department.

STAT 497 - Reading in Statistics

This course is designed to give credit to mature and able students for reading in areas not covered by courses, under the supervision of a staff member. A student, or group of students, wishing to use this course should find a staff member willing to supervise the proposed reading program. A detailed description of the material to be covered should be submitted to the Chair of the Department Honors Committee. (This should include a description of testing methods to be used.) The program will require the approval of both the Honors Committee, and the Chair of the Department. The students' mastery of the material of the course will be tested by a written or oral examination. This course may be taken in Fall or Winter and may be taken any number of times, subject always to the approval mentioned above. Prerequisite: Any 300-level STAT course.

Browse more courses taught by Adam Kashlak

Adam B Kashlak, Prachi Loliencar, Giseon Heo

Journal of Machine Learning Research. 2023 January;