TCH COM 5001: Artificial Intelligence and Communication

This course focuses on theoretical constructs associated with artificial intelligence, communicating with artificial intelligence, and smart technology communications. Students will master terminology, technologies, and hands-on exercises designed to provide an understanding of the history, present, and future of communication between humans and intelligent devices and among intelligent devices.

TCH COM 6001: Advanced Writing for Business

TCH COM 6001: Social Movements Online

TCH COM 3001/5001: AI to Zines

At the intersection of writing, design, and technology, AI to Zines and the Future of Technical Communication offers an experiential approach to technical communication through the study and use of artificial intelligence (AI) assisted communication design and do-it-yourself (DIY) zine-making. Students will examine how AI, as an emerging tool in content creation, and zine-making, as a DIY grassroots method of communication design, represent two contrasting yet complementary forces shaping the future of technical communication. Through practical community-based projects, students will use generative AI design tools and zine-making techniques to produce innovative forms of advocacy for challenging traditional power dynamics in science, technology, and society. By engaging critically with both high-tech (AI) and low-tech (zines) methods, students will learn to navigate the complexities of creating and distributing meaningful content in an increasingly automated world.

Stat 5364: Causal Data Science

Potential outcomes, randomized experiments, observational studies, statistical thinking, effect modification, interaction, causal directed acyclic graphs, Judea Pearl’s Theory of Causality, adjustment for confounding, selection bias, measurement bias, standardization, difference-in-differences, the front door method, instrumental variables, propensity-score methods and targeted learning.

Stat 5270: Foundations of Statistical Learning

An introduction to the theoretical aspects of statistical learning techniques commonly used in Data Science, Data Mining and the analysis of Big Data. Topics shall include linear regression, linear methods for classification, spline and kernel methods, lasso and ridge regression, neural networks, tree-based methods, model assessment and selection, nearest neighbors methods, model inference and averaging, support vector machines, bagging, boosting and bumping.

Math 5001: Scientific Programming with Python

This course provides an introduction to scientific programming using Python and its key libraries for data science. Students will gain hands-on experience in utilizing computational methods to define, program, and solve a range of mathematical problems. They will also learn how to generate reports and visualizations to effectively communicate their results. No prior knowledge of Python is required, and all mathematical concepts will be thoroughly explained throughout the course.

Mech Eng 5001 / Aero Eng 5001, Introduction to Design Optimization

This course presents the theoretical foundation of engineering optimal design and elaborates on solving engineering design problems using optimization techniques and machine learning methods. The outcomes of this course are: (1) an ability to formulate relevant engineering problems into optimization architectures; (2) an ability to identify appropriate optimization schemes for different optimal design applications; (3) an ability to leverage widely accepted machine learning strategies for facilitating design optimizations. This course also helps students improve their programming skills dealing with data and simulation, especially within MATLAB or Python. This course is lecture based with group projects involved.

Comp Sci 5111: Bridge to Advanced Computing

This course prepares science and engineering students, or those with insufficient computing background, for a pathway to a Master of Science at Missouri S&T. Topics covered include Problem Solving with Computing, Discrete Mathematics, Data Structures, Programming Tools (e.g., Github), and involves hands-on practice in multiple programming languages (e.g., Python, PyTorch).

Comp Sci 5206: Probability and Computing

This course covers fundamentals of probability and random processes with applications to computing and data analysis. Topics discussed will span five modules: Fundamentals, Concentration of measure, Convergence of random processes, Markov Chains, and Martingales.

Comp Sci 5411 Natural Language Processing

Natural Language Processing (NLP) is a dynamic field that focuses on the interaction between computers and human language. This course provides a deep exploration of NLP concepts, tools, and methodologies from foundational concepts such as part-of-speech tagging, syntactic structure and dependency parsing, language modeling to state-of-the-art advancements in machine translation and language generation, transformers and pre-trained large language models.

Comp Sci 5421: Reinforcement Learning

This course introduces the fundamentals of Reinforcement Learning (RL) theory from a Machine Learning perspective. Iterative algorithms to solve problems in RL will be described. Exploration strategies based on Upper Confidence Bounds and Monte Carlo sampling will be discussed. Sample complexity of algorithms will be theoretically analyzed and quantified.

Comp Sci 5480 Deep Learning

This course introduces reinforcement learning and artificial neural networks as the foundation for deep learning and covers deep learning architectures, including deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks. Students will implement course concepts in intensive programming assignments.

Comp Sci 6411 Large Language Models

This course focuses on the cutting-edge research topics surrounding pre-trained large language models (LLMs). Key topics include the technical foundations of LLMs (BERT, GPT, T5 models, mixture-of-expert models, retrieval-based models etc.), emerging capabilities (knowledge, reasoning, few-shot learning, in-context learning), post-training: instruction fine-tuning, alignment, reinforcement learning with human feedback (RLHF) and constitutional AI. The course will also cover metacognitive capabilities of LLMs alongside critical discussions on security and ethical considerations.