J. Wilder Lavington

J. Wilder Lavington

Machine Learning and Artificial Intelligence Researcher

Teaching

Teaching Assistent

Course, University of British Columbia Vancouver, Department of Computer Science, 2019

Machine Learning and Data Mining

We introduce basic principles and techniques in the fields of data mining and machine learning. These are some of the key tools behind the emerging field of data science and the popularity of the `big data’ buzzword. These techniques are now running behind the scenes to discover patterns and make predictions in various applications in our daily lives. We’ll focus on many of the core data mining and machine learning technlogies, with motivating applications from a variety of disciplines.

Teaching Assistant

Course, University of Colorado Boulder, Department of Applied Mathematics, 2018

Precalculus

Prepares students for the challenging content and pace of the calculus sequence required for all engineering majors. Covers algebra, trigonometry and selected topics in analytical geometry. Prepares students for the calculus courses offered for engineering students. Requires students to engage in rigorous work sessions as they review topics that they must be comfortable with to pursue engineering course work. Structured to accustom students to the pace and culture of learning encountered in engineering math courses

Teaching Assistant

Course, University of Colorado Boulder, Department of Applied Mathematics, 2017

Calculus 3

Covers multivariable calculus, vector analysis, and theorems of Gauss, Green, and Stokes.

Teaching Assistant

Course, University of Colorado Boulder, Department of Applied Mathematics, 2016

Matrix Methods

Introduces linear algebra and matrices with an emphasis on applications, including methods to solve systems of linear algebraic and linear ordinary differential equations. Discusses vector space concepts, decomposition theorems, and eigenvalue problems.

Teaching Assistant

Course, University of Colorado Boulder, Department of Applied Mathematics, 2015

Scientific Computing

Focuses on numerical solution of nonlinear equations, interpolation, methods in numerical integration, numerical solution of linear systems, and matrix eigenvalue problems. Stresses significant computer applications and software.

Teaching Assistant

Course, University of Colorado Boulder, Department of Applied Mathematics, 2014

Applied Probability

Studies axioms, counting formulas, conditional probability, independence, random variables, continuous and discrete distribution, expectation, moment generating functions, law of large numbers, central limit theorem, Poisson process, and multivariate Gaussian distribution. Prereq., APPM 2350 or MATH 2400. Students may not receive credit for both APPM 3570 and either ECEN 3810 or MATH 4510.