CDS Core Courses

AAE 59000 – Data Science Applications in Mechanics of Materials 
Topics vary – projects in Aeronautical Engineering. Permission of instructor required. 

ABE 53100 – Instrumentation and Data Acquisition 
This course educates students in the use, selection, and design of instrumentation and data acquisition for agricultural, food, environmental, and biological systems. Emphasis is on measurement of position (GPS), force, pressure, power, torque, flow, and temperature along with environmental sensors. Labs focus on building and using measurement systems and programming PC computers for data acquisition and analysis. 

AT 60700 – Aviation Applications of Bayesian Inference 
This course provides a critical foundation necessary for understanding Bayesian theory and employing that theory in the analysis of typical data generated in industry. The course will focus specifically on solutions of aviation-related problems through data analysis. Material includes overview and implementation of relevant statistical software applications. Practical skills in presenting advanced analyses to both professional and scientific audiences is a key component of the course. 

BCHM 61200 – Bioinformatic Analysis of Genome Scale Data 
This course provides a hands-on experience for life science researchers in the bioinformatic analysis of genome-scale data. The various disciplines in the life sciences are generating a wealth of experimental and annotation data. Today’s graduate students need experience with modern tools that can help them to access, explore, analyze, interpret and manage the data that they generate in the lab. Students will use the R programming language and packages from Bioconductor, the R bioinformatics project, as their principal tools for this course. Students will develop workflows in R that bridge established algorithms for bioinformatics such as limma, edgeR or DESeq2, incorporating methods to import, QC, transform and visualize genome-scale datasets derived from next generation sequencing experiments. A critical aspect of bioinformatics that is often inadequate is workflow documentation. This course will use Rmarkdown to integrate computer code, data and results to manage complex bioinformatics projects. The class has lecture, lab and distance components. Lectures will focus on the theoretical and biological aspects of bioinformatics analysis using recent examples from the literature. In lab, students will work on programming exercises or projects using published datasets. Advanced students will also have the opportunity to work with their own data. Distance instruction will include R tutorials and videos that students can work through at their own pace (subject to completion deadlines). Particular emphasis will be placed on the theoretical and practical limitations of next generation sequencing data. No prior computer programming experience is required, but it is assumed that students have a firm grasp of the fundamental principles of molecular biology and how they relate to complex processes such as gene expression and genome organization. 

CE 50701 – Geospatial Data Analytics 
The course will introduce fundamental theories, analytical methods and programming skills that are needed to work with geospatial data. Students will learn the theories, methods, and techniques to visualize, analyze and model various geospatial data through hands-on computer programming practice based on various open source geospatial libraries. To be specific, the course will use R and its related packages as the basic tool for implementation. The goal is to enable the learners to develop their own geospatial analytical applications. 

CE 59700 – Image-based Sensing 
Hours and credits to be arranged. Permission of instructor required. 

EAPS 50700 – Introduction to Analysis and Computing with Geoscience Data 
Course teaches computing techniques including error analysis, line and surface fitting, interpolation, map projections, geospatial and temporal correlations, signal processing, and visualization with discussions on specific and practical geoscience applications. Lectures with computer exercises and team project reporting using open-source computer software 

EAPS 51500 – Geodata Science 
Course covers a range of topics with applications of mathematical, statistical, numerical, and distributed parallel computing methods for modeling and understanding complex and large spatio-temporal geoscience datasets in the formats common to in-situ observations, asynoptic remote sensing data, volumetric gridded analysis, etc. 

FNR 57400 – Big Data, AI, and Forests 
This course is focused on introductory big data analysis, artificial intelligence, and associated applications in large-scale forest research. The lecture will cover the challenges we encounter in big data ecological research, and the approaches to overcome these challenges. Real-time forest inventory and wildlife survey data at national and continental levels will be utilized in this course, and actual high-impact research projects will be introduced as case studies to inform students of the state-of-the-art in this subject area. High-performance computing clusters will be utilized for big data analysis. This course is also open to non-forestry majors. We will introduce basic machine learning techniques that are applicable to other subject areas. Guest lectures may cover big data analyses in different fields, internet-of-things, and/or data management and optimization/decimation for collaborative Virtual Reality experiences. The class will be evaluated through a final project, for which students will work independently or in a group setting to develop a ‘mini’ research manuscript with a title of their own selection. All the groups are encouraged to submit their manuscript for publication at peer-reviewed journals, and those whose manuscripts have passed the initial journal screening will get extra bonus points. 

HSCI 52500 – Statistics and Computational Approaches for Health Sciences 
Statistical methods are important for data analysis and understanding the trends in the dataset. This course will provide an introduction to the analysis of biological data in a statistical framework using standard computational methods. The course will have a lecture and hands-on component to introduce students to topics and then utilize them to solve typical problems in Health Sciences. In addition to learning the statistical concepts, the students will also be introduced to computational approaches for data analysis. The combination of statistical and computational concepts along with the hands-on experience will help students in their research projects. The topics covered include data representation, sample statistics, probability, common discrete and continuous distribution, confidence interval estimation, experimental design, analysis of variance, statistical methods for hypothesis testing, linear and logistic regression, correlation, power analysis, graph theory, network analysis, omics-based analysis, and data visualization. The course is ideal for Health Sciences students who perform data analysis and are interested in implementing these approaches in their research. 

TDM 51100 – Corporate Partners 
Students in The Data Mine Corporate Partners Learning Community will work in groups with Corporate Partner mentors on a variety of projects. They will analyze real data related to questions that the Corporate Partner proposes. Most projects will last for a full academic year (late August through late April), with multiple reports and presentations given more frequently. The mentor is expected to meet with the students weekly by Microsoft Teams, or (more rarely) in person. Students are expected to actively participate in these meetings and in all individual and group work. The goal of the course is to help students build impactful industry-related skills in data science, visualization, and data engineering. The Data Mine staff also has data scientists who can assist students with technical questions focused on the skills being built and the research conducted. Students can work on real-world industry facing issues that have a high value add for the corporate partner. 

BME 64600 – Deep Learning 
This course teaches the theory and practice of deep neural networks from basic principles through state-of-the-art methods. The class blends hands-on programming, using a variety of state-of-the-art programming frameworks, with theoretical treatment based on current literature. Implementation will emphasize the use of the Pytorch language and the use of dynamic computational graphs. Some previous experience with optimization techniques is important for success in the course. 

CS 57100 – Artificial Intelligence 
Artificial Intelligence (AI) systems are increasingly being deployed in many real-world tasks. This course provides an introduction to the fundamental principles and applications of AI. The course covers classic material including search-based methods, probabilistic reasoning, game playing, decision making, exact and approximate inference, causal learning, and reinforcement learning as well as selected advanced topics. The focus of the course is on foundational methods and current techniques for building AI systems that exhibit ‘intelligent’ behavior and can ‘learn’ from experience. The course assumes students are familiar with basic concepts in analysis, linear algebra, optimization, discrete mathematics, elementary probability, statistics, data structures, and algorithms. Students are expected to have good programming and software development skills and have a working knowledge of Python and Java. 

CS 57700 – Natural Language Processing 
This course will cover the key concepts and methods used in modern Natural Language Processing (NLP). Throughout the course several core NLP tasks, such as sentiment analysis, information extraction, syntactic and semantic analysis, will be discussed. The course will emphasize machine-learning and data-driven algorithms and techniques, and will compare several different approaches to these problems in terms of their performance, supervision effort and computational complexity. 

CS 57800 – Statistical Machine Learning 
This introductory course will cover many concepts, models, and algorithms in machine learning. Topics include classical supervised learning (e.g., regression and classification), unsupervised learning (e.g., principle component analysis and K-means), and recent development in the machine learning field such as variational Bayes, expectation propagation, and Gaussian processes. While this course will give students the basic ideas and intuition behind modern machine learning methods, the underlying theme in the course is probabilistic inference. 

CS 58700 – Foundations of Deep Learning 
This course provides an integrated view of the key concepts of deep learning (representation learning) methods. This course focuses on teaching principles and methods needed to design and deploy novel deep learning models, emphasizing the relationship between traditional statistical models, causality, invariant theory, and the algorithmic challenges of designing and deploying deep learning models in real-world applications. This course has both a theoretical and coding component. The course assumes familiarity with coding in the language used for state-of-the-art deep learning libraries, linear algebra, probability theory, and statistical machine learning. 

CS 59300 – Computer Vision with Deep Learning 
A variable title course for topics not currently covered in the CS graduate curriculum. Each offering follows a traditional course structure with textbook(s), assignments, exams, and week-by-week content synopsis described in a syllabus. 

CS 59300 – Machine Learning Theory 
A variable title course for topics not currently covered in the CS graduate curriculum. Each offering follows a traditional course structure with textbook(s), assignments, exams, and week-by-week content synopsis described in a syllabus. 

ECE 57000 – Artificial Intelligence 
Introduction to the basic concepts and various approaches of artificial intelligence. The first part of the course deals with heuristic search and shows how problems involving search can be solved more efficiently by the use of heuristics and how, in some cases, it is possible to discover heuristics automatically. The next part of the course presents ways to represent knowledge about the world and how to reason logically with that knowledge. The third part of the course introduces the student to advanced topics of AI drawn from machine learning, natural language understanding, computer vision, and reasoning under uncertainty. The emphasis of this part is to illustrate that representation and search are fundamental issues in all aspects of artificial intelligence. 

ME 53900 – Introduction to Scientific Machine Learning 
Introduction to the fundamentals of predictive modeling for advanced undergraduates and graduate science and engineering students that work in the intersection of data and theory. 

CNIT 60100 – Applied Statistics in Information Technology 
This course will survey the field of applied statistics in information technology. Students will gain hands-on experience running statistical analyses on samples from a variety of populations. Students will learn the process of data cleaning, data transforming/coding, identifying the appropriate statistical analyses (descriptive and inferential), as well as writing and interpreting the results. Specifically, this course will survey the following statistical approaches: correlations, t-test, analysis of variance, analysis of co-variance, factorial ANOVA, simple regression, multiple regression, logistic regression, chi-square analysis, factor analysis, and nonparametric tests. In this course, students will be able to differentiate statistical analyses and identify appropriate statistical analyses depending on the research question and variables of interest. 

MA 51900 – Introduction to Probability 
Algebra of sets, sample spaces, combinatorial problems, independence, random variables, distribution functions, moment generating functions, special continuous and discrete distributions, distribution of a function of a random variable, limit theorems. 

MA 53200 – Elements of Stochastic Process 
(STAT 53200) A basic course in stochastic models, including discrete and continuous time Markov chains and Brownian motion, as well as an introduction to topics such as Gaussian processes, queues, epidemic models, branching processes, renewal processes, replacement, and reliability problems. 

STAT 51200 – Applied Regression Analysis 
Inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data, nonlinear regression. One-way and two-way analysis of variance, multiple comparisons, fixed and random factors, analysis of covariance. Use of existing statistical computer programs. 

STAT 52500 – Intermediate Statistical Methods 
Statistical methods for analyzing data based on general/generalized linear models, including linear regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), random and mixed effects models, and logistic/loglinear regression models. Application of these methods to real-world problems using SAS statistical software. 

STAT 52600 – Advanced Statistical Methods 
As a sequel to STAT 52500, this course introduces statistical modeling tools for situations where standard least-squares techniques may not apply. This includes an extensive coverage of generalized linear models (GLM) for non-Gaussian responses, mixed effects models to describe correlated data, nonparametric regression, and lastly, parametric and nonparametric survival models for the analysis of (possibly censored) time-to-event data. Among issues to be discussed are the estimation of the models, the testing of hypotheses, and the checking of model adequacy. Data examples will be used throughout the course to illustrate the methodologies and the related software tools in R. 

STAT 52900 – Applied Decision Theory and Bayesian Statistics 
Bayesian and decision-theoretic formulation of problems; construction of utility functions and quantifications of prior information; methods of Bayesian decision and inference, with applications; empirical Bayes; combination of evidence; Bayesian design and sequential analysis; comparisons of statistical paradigms. 

STAT 54500 – Introduction to Computational Statistics 
This introductory course covers the fundamentals of computing for statistics and data analysis. It starts with a brief overview of programming using a general purpose compiled language (C) and a statistics-oriented interpreted language (R). The course proceeds to cover data structures and algorithms that are directly relevant to statistics and data analysis and concludes with a computing-oriented introduction to selected statistical methods. A significant part of the course involves programming and hands-on experimentation demonstrating the covered techniques, ration, and Markov chain Monte Carlo methods. 

STAT 54600 – Computational Statistics 
The course focuses on two fundamental aspects in computational statistics: (1) what to compute and (2) how to compute. The first is covered with a brief review of advanced topics in statistical inference, including Fisher’s fiducial inference, Bayesian and frequentist methods, and the Dempster-Shafer (DS) Theory. The second is discussed in detail by examining exact, approximation, and interactive simulation methods for statistical inference with a variety of commonly used statistical models. The emphasis is on the EM-type and quasi-Newton algorithms, numerical differentiation and integration, and Markov chain Monte Carlo methods.