As of July 1st, 2020, what was previously the Department of Mathematics and Computer Science has been restructured into two new departments, the Department of Computer Science, and the Department of Mathematics and Statistics.

Other News and Announcements

Adjunct Positions in the Department of Mathematics and Statistics

Job Description
The Department of Mathematics and Statistics at the University of Missouri-St. Louis is seeking qualified candidates for adjunct instructor positions for the Spring 2022 semester. Courses may include Intro to Contemporary Mathematics, College Algebra, Trigonometry, Basic Calculus, Intro to Probability and Statistics, Calculus I, II, and III, Differential Equations Elementary Linear Algebra, and more.  Adjunct appointments are term, non-benefit, non-tenure-accruing positions. Appointment periods are for the semester, with the possibility of renewed appointment based on performance.

Minimum Qualifications
A PhD is preferred, but adjunct instructors must have a minimum of a master’s degree in mathematics or closely related discipline.

Full Time/Part Time

Application Deadline
December 1, 2021

Application Instructions
All applications need to be submitted online at . Applicants must combine all application materials (Letter of interest, curriculum vitae, statement of teaching philosophy, and list of references) into one PDF document and upload as a resume attachment. Limit document name to 50 characters, maximum size is 11 MB. Do not include special characters. Candidates will be required to successfully pass a Criminal Background Check prior to beginning employment.

About UMSL
Established as a campus of the University of Missouri in 1963, the University of Missouri-St. Louis has grown rapidly as the principal public university in the St. Louis area with a current enrollment of over 15,000 in day and evening sessions.  As part of a metropolitan area of almost 3,000,000 people, UMSL has immediate availability to cultural and educational institutions of one of the country’s major centers.  The area includes fine museums, libraries, theatre and opera companies, symphony orchestras, medical centers, athletic and recreational facilities, and a score of public and private universities and colleges. UMSL is an Anchor Institution that is committed to intentionally applying university assets in partnership with community to strengthen the local community and the St. Louis region.

The University of Missouri-St. Louis is an affirmative action, equal opportunity employer committed to excellence through diversity.

MATH 4890/5890 – ONLINE

Introduction to Artificial Neural Networks

Instructor: Adrian Clingher

Course Overview: In recent years, artificial neural networks have dramatically advanced the capabilities of computers to perform specialized tasks, such as image classification and processing, speech and handwriting recognition, medical data analysis, autonomous car navigation, fraud detection, natural language processing, game playing, and many others. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas and are widely deployed in industry, academia and government applications. This course introduces students to the mathematical ideas and techniques underlying artificial neural network models. Students will receive training and guidance that will teach them how and where this technology is currently being implemented and will think critically about future applications. Course topics may include: basics of supervised learning, loss functions, regularization, gradient descent optimization, feedforward neural networks, forward propagation, activation functions, cross-entropy loss, backpropagation algorithm, convolutional neural networks, building blocks, training, specialized architecture. The API Keras, via an R interface, will be used as computing environment.

Prerequisites: Basic statistics (Math 1320 or equivalent), multi-variable calculus (Math 2000 or equivalent), elementary linear algebra (Math 2450 or equivalent) or consent of the instructor. No prior programming experience is assumed.

Questions? Send an email to the instructor at

neural network class