DSE Program Course Descriptions
INDEX
| BIO | BMB | BMS | BUA | CIE | CIS | CMJ | COS | CYB | DIG | DSE |
| ECE | ECO | EHD | GEO | GIS | HTY | INT | PSE | PSY | SIE | SFR |
| SMS | STS | SVT |
To view UMaine official course descriptions, consult the Graduate Catalog > Graduate Courses > insert Prefix (e.g. COS, SIE, etc.) > Filter.
For the convenience of students, copies of graduate course descriptions from a recent catalog are also provided below.
BIO Graduate Course Descriptions
BIO 593 – Advanced Biometry
A course in advanced graduate statistics oriented towards the environmental sciences. This course is intended as a breadth-oriented survey course that will expose the student to all types of statistics one might encounter in environmental research. It will review and place into a more general context ANOVA and regression, cover philosophy of science/modes of statistics (Bayesian and Monte Carlo), random/mixed/hierarchical models, generalized linear models (including logistic and Poisson regression), modern regression (robust, non-linear, machine-learning), multivariate statistics, and spatial/temporal statistics.
Prerequisites & Notes: None
Credits: 4
BMB Graduate Course Descriptions
BMB 502 – Introduction to Bioinformatics
A multidisciplinary study of fundamental biological questions through the organization, integration and analysis of increasingly large and complex datasets. Topics include primary data repositories, data integration and curation, sequence analysis methods, functional annotation, high-throughput sequence analysis workflows, statistical analysis of gene expression data, clustering methods and modeling biological networks.
Prerequisites & Notes: BMB 280 or instructor’s permission.
Credits: 3
BMB 520 – Introduction to Image Analysis
The current expectations of rigor and reproducibility in biomedical sciences require quantification of results obtained through microscopy. This course introduces students to the basics of working with the digital microscopy images and focuses on the quantification of fluorescence microscopy data using ImageJ and MATLAB.
Prerequisites & Notes: None.
Credits: 3
BMS Graduate Course Descriptions
BMS 625 – Foundations of Biomedical Science & Engineering
Course provides an overview of fundamental/critical issues in biomedical science and engineering today.
Prerequisites & Notes: None
Credits: 1-4
Supplemental Note: 1 credit of Computational Bio / Biostats
BUA Graduate Course Descriptions
BUA 680 – Foundations of Business Intelligence and Analytics
This course presents the philosophical and technical foundations of business intelligence and analytics. The philosophical principles of business intelligence and analytics are discussed. Important fundamental concepts and tools in business intelligence and analytics are introduced using a structured and integrated approach that moves from initial data collection to final decision outcome assessment. Throughout the course, conditional reasoning and logical thinking in terms of process and systems are emphasized. Sample Syllabus
Prerequisites & Notes: MBA student or permission from Business School Office of Graduate Programs. Must be in a graduate degree or certificate program.
Credits: 3
BUA 681 – Data Management and Analytics
This course introduces students to different types of data commonly collected in business settings. Students will also learn basic skills of managing and wrangling the business data using analytical techniques like structured query language and data visualization in R, an environment for statistical computing and visualization. Knowledge of basic statistics through linear regression is helpful, but not necessary. The course assumes students have had no previous exposure to computer programming. Sample Syllabus
Prerequisites & Notes: BUA 601 or instructor permission
Credits: 3
BUA 682 – Data Pre-Processing for Business Analytics
This course is designed to enhance student’s understanding of data quality problems commonly encountered in business environments including but not limited to missing data, noisy data, and data biases. This course discusses mechanisms of these problems and their impact on data analysis and modeling results and presents how to solve these problems by using different data pre-processing techniques such as imputation, integration, normalization, and transformation. Students practice these techniques with business data sets using mainstream analytical software. Sample Syllabus
Prerequisites & Notes: BUA 680 or permission
Credits: 3
BUA 683 – Information Visualization
This course presents a variety of data visualization techniques to graphically summarise business data information. Students will learn to create charts, maps and other visualizations to create effective graphical displays of business data that tell meaningful business stories. Students will also learn to critically evaluate examples from print media and the internet after learning the foundations of information visualization. Sample Syllabus
Prerequisites & Notes: BUA 601 or equivalent, or permission. MBA student or permission from Business School Office of Graduate Programs. Must be in a graduate degree or certificate program.
Credits: 3
BUA 684 – Business Data Mining and Knowledge Discovery
This course introduces students to a variety of cutting-edge mining methods for the purposes of supervised learning and unsupervised learning. Students will apply these methods to analyze data in different business functional areas such as marketing, accounting/finance, operation, and management across industry sectors. The course emphasis is on learning valuable data information from the data analysis results and discovering interpret able and meaningful knowledge that can support better business decision making. Mainstream analytical software is used intensively to analyze real business datasets. Sample Syllabus
Prerequisites & Notes: BUA 380 or permission
Credits: 3
BUA 685 – Problem Solving and Decision Analysis
This course is dual focused on business problem formulation and decision analysis. First, the course introduces students to a variety of ways to formulate a business problem and identify its decision alternatives using systems thinking and process thinking. Second, the course presents core concepts and techniques for conducting data-driven decision analysis (e.g. utility/objective function, linear/nonlinear optimization, and simulation optimization) with purpose of recommending optimal decision options by taking advantage of the results of predictive analytics. Sample Syllabus
Prerequisites & Notes: BUA 685 or permission, MBA student or permission from Business School Office of Graduate Programs. Must be in a graduate degree or certificate program.
Credits: 3
BUA 686 – Predictive and Business Forecasting
This course presents a set of topics in developing analytical methodologies that make prediction and forecasting about future events of interest to individual business and industry in general. Students are introduced to managerial techniques and analytical models that reveal valuable relationships in economic and business data for supporting short-term and long-term planning. Students will learn how to build the models, how to interpret the predictions and forecasts produced from the models, and how to evaluate the reliability of the model results. Sample Syllabus
Prerequisites & Notes: BUA 601 or equivalent or permission. MBA student or permission from Business School Office of Graduate Programs. Must be in a graduate degree or certificate program.
Credits: 3
CIE Graduate Course Descriptions
CIE 598 Civil Engineering Systems and Optimization
Formulation of decision-making problems at different hierarchical levels for engineering systems. Topics include formulation of linear, integer and non-linear models; introduction to exact and approximate solution techniques; solution interpretation and sensitivity analyses; network terminology and problems; basics of game theory; multi-objective models, pareto front and decision analysis; performing simulation analysis; analyzing simulation outputs; queuing analysis; and transportation systems. Includes applications in civil engineering, transportation engineering, structural engineering, project selection, networks, allocation, routing/scheduling, and distribution.
Prerequisites & Notes: MAT 126, MAT 127, or instr permission
Credits: 3.
CIS Graduate Course Descriptions (Univ of Maine – Augusta)
CIS 450 – Data Mining
This course in data mining techniques is designed for both computer information systems majors and business administration majors. In this course, students will explore and analyze data to support business intelligence applications. Methods used include cluster analysis, decision trees, classification of data, estimation and prediction, and association techniques. The goal of data mining is to take data and convert collected data into information readily usable by business managers to determine buying behavior, fraud detection, database marketing, market basket analysis, and information management. (This course is cross-listed with BUA 450 and DSC 450 and MAT 450)
Prerequisite(s): MAT 115, and CIS 255 or CIS 303 or CIS 330 or CIS 449 or permission of instructor.
Credits: 3
Supplemental Note: This course co-listed as CIS450 / BUA450/ DSC450 / MAT450.
CIS 461 – Spatio-Temporal Information Science
Space and time are fundamental concepts of how humans process information and seek to understand data. This course offers the theoretical issues and applied practices that can impact our computational understanding of space and time. Topics covered include databases, spaces, modeling, representation, algorithms, data structures, architectures, interfaces, reasoning, and uncertainty in both space and time. (This course is cross-listed with DSC 461)
Prerequisite(s): CIS 360
Credits: 3
Supplemental Note: This course co-listed as DSC 461.
CMJ Graduate Course Descriptions
CMJ 593 – Topics in Communication: Social Media and Digital Culture
Advanced study of selected topics.
This course explores the digital cultures created through social media, the processes that go into their creation, and the impact these cultures have on society at large. We will take a close look at how participation in social media such as Facebook, Twitter, and Instagram contributes to these collaborative, user-driven cultures, and examine the extent to which these cultures interact, impact, and reflect mainstream cultures.
The creation of digital cultures is inexorably tied to identity performance and the conceptualization of social media as a potential virtual public sphere. Social media platforms are also often seen as a space for marginalized groups to connect, be heard, and influence dominant narratives. At the same time, corporations, hackers, and trolls are active players on social media sites as well, creating a space that is characterized by a wide array of voices and goals. In this course, we will examine the role these different variables play in the creation of digital cultures through a consideration of relevant theories, extant research, and specific case studies. Sample Syllabus
Prerequisites & Notes: permission
Credits: 3
Supplemental Note: normally offered biannually in Fall in odd years.
CMJ 601 – Seminar in Research Methods
Advanced study of research methodologies appropriate for quantitative and qualitative studies of speech, language, and communicative behavior. Emphasis is on research questions, assumptions, designs, and procedures for experimental and descriptive studies in communication. Sample Syllabus
Prerequisites & Notes: permission.
Credits: 3
Supplemental Note: normally offered biannually in Fall in even years.
COS Graduate Course Descriptions
COS 535 Engineering Privacy in Software Systems
Introduces theory and practice for privacy, anonymity and compliance. Topics include:information privacy and multi-jurisdictional privacy compliance, privacy governance frameworks, privacy engineering lifecycle methodology, privacy by design, usable privacy, privacy and emerging technologies, anonymity techniques, differential privacy and private AI. Sample Syllabus
Prerequisites & Notes: college level knowledge of IT or software development
Credits: 3
COS 541 Cloud Computing
The National Institute of Stands and Technology (NIST) defines cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” This course will study the technologies underpinning the rapid expansion of this new computing paradigm, the new problem-solving capabilities enabled by the cloud, and provide the student with hands-on experience in utilizing cloud services for scientific research. It will focus on the virtualization of computational resources, cloud storage models, distributed computing in the cloud, and important applications areas such as big data analytics. Sample Syllabus
Prerequisites & Notes: COS 331 or equivalent
Credits: 3
Supplemental Note: normally offered biannually in Spring
COS 565 – Data Visualization
Introduction to the goals, techniques, implementation and evaluation of visual representations for large quantities of data. Students work with a team to produce a novel visualization solution for a client with application domain data and goals.
Prerequisites & Notes: COS 226 or permission of instructor.
Credits: 3
COS 470/570 – Topics in Artificial Intelligence
Introduces the student to the field of artificial intelligence, including fundamental areas and concepts such as heuristic search, knowledge representation, automated reasoning and planning, deep learning, intelligent agents, and multiagent systems. Experience in AI programming is provided by homework assignments and a semester project. Sample Syllabus
Prerequisites & Notes: knowledge of programming at level of COS 225 and data structures at level of COS 226 or permission of instructor
Credits: 3
COS 573 Computer Vision
Practical introduction to machine learning. Computer Vision is an accessible sub-field of computer science rising in importance and accelerating on the strengths of machine learning methods that have become the 21st century model for artificial intelligence. In this course, we will explore the uses of tools and techniques to understand our world through computing using images as our data. The first half of the course will introduce machine learning and convolution neural networks for object recognition and classification, photogrammetry and reconstruction, and multimodal and hyperspectral imaging. As the course progresses, we will delve into the topics of image acquisition, mathematical analysis, the Fourier transform and frequency space, statistical pattern recognition, and other foundations of the field. This course is a fast-paced, hands-on, practical exploration of computer vision. Students from the class are organized into teams to work on a computer vision project. Sample Syllabus
Prerequisites: COS 226 Intro to Data Structures
Credits: 3
COS 575 Machine Learning
The course addresses the foundations of machine learning with applications in real-world problems. The ML techniques recognize hidden patterns that often provide dramatic competitive advantages with significant computational improvements in learning tasks never imagined before. By the end of 2020, IT departments will be monitoring 50 times more data than they are today. This tidal wave of data has driven unprecedented demand for those with the skills required to manage and leverage these very large and diverse data sets. Advances in ML are helping to learn the required skills and solve a very broad variety of problems in many fields including: engineering and AI, computer vision, advertising, health care, social media, robotics, economics, security, agriculture, and cool new industries like self-driving drones/cars and highly efficient automated homes. Our goal in this course is to help you to Understand fundamentals of ML, Learn technical details of ML algorithms, Learn how to implement some important algorithms, Use ML algorithms for your research and applications. COS575SampleSyllabus, COS475SampleSyllabus
Prerequisites & Notes: MAT 126, MAT 127, STS 232 (or STS 434 or STS 332 or STS 435) Further, a good background in the following topics is required:
• Matrix operations, rank, eigenvalue/vector, nullity, linear independence, inner products, orthogonality, positive (semi-) definite matrices, eigenvalue decomposition.
• Jointly distributed random variables, multivariate densities and mass functions, expectation, independence, conditional distributions, Baye rule, the multivariate normal distribution.
• Partial derivatives, gradients, chain rule.
Course Organization: The course is split into two parts: 1) ML algorithms and their theoretical
analysis, and 2) Applications of ML techniques in real-world problems. The class has a final project, which will provide you with the opportunity to apply the material to an advance topic of your interest or even your own research.
Credits: 3
COS 598 (COS 5xx) Interpretability and Explainability in Machine Learning (tentative – proposed for inclusion)
This course will familiarize students with recent advances in interpretable and explainable machine learning. To be discussed includes seminal papers of the field, the notion of interpretability and explainability, traditional interpretable models, post-hoc model interpretability analysis, and interpretability in deep learning. This course will also introduce various applications that will benefit from model interpretability and explainability, such as healthcare and finance. Sample Syllabus
Prerequisites & Notes: COS 575/475.
Credits: 3
COS 580 – Topics in Database Management Systems
This course covers database systems from the perspective of database designers and programmers, different from database system implementers. The emphasis is on fundamental topics that should be familiar to every computer scientist and good programmer. The course covers topics such as Entity-Relationship modeling, relational database design theory, relational algebra and calculus, SQL, Datalog, object-oriented and object-relational databases, with OQL and SQL3, and semistructured databases, with XQuery.
Prerequisites & Notes: permission.
Credits: 3
COS Graduate Course Descriptions (Univ of Southern Maine)
COS 522 – Computing for Data Science
This course provides a practical introduction to the data science workflow using Python. Successful completion of the course will involve using advanced features of Python, retrieving information in data files, working with numpy and pandas library, visualizing information and completing an end to end data science project.
Prerequisites & Notes: permission.
Credits: 4
COS 573 – Deep Learning
An introduction to the theory and applications of deep learning. Topics include basic neural networks, convolutional and recurrent networks, and applications in computer vision and language interpretation. Students will learn to design neural network architectures and training procedures via hands-on assignments. (Sample Syllabus)
Prerequisites & Notes: permission.
Credits: 4
COS 575 – Machine Learning
The basic theory, algorithms, and applications of Machine Learning are covered in this course. Students will develop an understanding of learning theory, supervised and unsupervised learning algorithms, and reinforcement learning techniques. The course will also explore recent practical applications of machine learning.
Prerequisites & Notes: permission.
Credits: 4
CYB Graduate Course Descriptions (Univ of Maine – Augusta)
CYB 501 – Cybersecurity Fundamentals
This course explores the fundamental concepts of Cybersecurity. The course will examine risk assessment and policy development to protect enterprise assets. The course will examine the basic security requirements of modern computing environments and the defense tools and methods user to apply a cyber-defense. The role of cryptology to protect information, access control methods, business continuity, and disaster recovery will be discussed. Topics will include incident response, secure design principles for networks and software, digital forensics, security operations, and the legal aspects of security.
Prerequisite(s): Graduate Standing
Credits: 3
CYB 520 – Cybersecurity Policy and Risk Management
This course provides a review of the topic of risk management and how risk, threats, and vulnerabilities impact information systems. It explores general methodologies used to assess and manage risks to information security based on defining an acceptable level of risk within policies. The student will learn to apply activities for risk assessment and identification, and risk mitigation through policy implementation.
Prerequisite(s): None listed
Credits: 3
CYB 581 – Cybersecurity Investigations
This course will examine how digital evidence is gathered, handled, and admitted to court. The course will focus on the forensic process and adherence to the law of legally obtaining digital evidence that will be admissible in court. Topics will include conduction forensic investigations on computer systems, mobile devices, networks, embedded devices, Internet of Things devices, documenting evidence, preparing a forensics report, and how to prepare for presenting evidence at a trial.
Prerequisite(s): Graduate Standing
Credits: 3
DIG Graduate Course Descriptions
DIG 500 – Introduction to Digital Curation
As the introductory course to the Digital Curation program, this class surveys the variety of digital artifacts that we consciously or unconsciously create and consume today, with a focus on how to collect and manage digitized and born-digital artifacts and their related data. Students learn technical skills such as how to digitize analog documents, photographs, and videos, as well as curatorial knowledge such as how selection criteria vary as a function of type of institution (archives v. libraries v. museum) and field (art v. archeology). The course also reviews methods for ensuring the ongoing integrity of the artifact and laws governing the acquisition and use of intellectual property, such as how copyright extends to images, editions, and future versions of a work. Sample Syllabus
Prerequisites & Notes: None
Credits: 3
DIG 510 – Metadata Systems
This course covers digital formats for describing the contents and contexts of artifacts with an emphasis on their use in libraries, archives, and online repositories. This includes a discussion on the need for and use of metadata in a variety of digital contexts, exposure to specific metadata standards used in a number of fields, and demonstrations of how these metadata are expressed in several output formats. Sample Syllabus
Prerequisites & Notes: DIG 500 strongly recommended
Credits: 3
DIG 550 – Digital Preservation
This course acquaints students with the challenges of, and best practices for, preserving digital artifacts. Topics include a survey of the (sometimes bewildering) array of formats for digital media, along with their vulnerabilities and half-lives; analysis of various preservation strategies (storage, migration, emulation, reinterpretation); institutional, legal, and practical impediments to preservation; preservation standards and resources for digital media (Media Matters, Variable Media Questionnaire). Sample Syllabus
Prerequisites & Notes: DIG 500, 510, and 540 strongly recommended
Credits: 3
DSE Graduate Course Descriptions
DSE 501 Statistical Foundations of Data Science and Engineering
An introduction and overview of statistical methods that are fundamental in understanding data science. Topics include: basic probability theory, Importing, Summarizing, and Visualizing Data, Basic Probability Theory, Random Variable, Statistical Models, Expectation and Variance, Descriptive statistics, Frequentist Statistics, Monte Carlo Method, Hypothesis Testing, Confidence Intervals, Bayesian Probability, Decision Trees, Time-series/Sequential Data Science. Sample Syllabus
Prerequisites and notes: college level statistics course
Credits: 3
DSE 502 Programming Foundations for Data Science and Engineering
Coding and understanding of basic programming techniques are essential for students regardless of their field of studies. This course is tailored for graduate students with little to no previous programming experience that have a need for practical programming skills. Firstly, this course will introduce basic programming concepts (variables, conditions, loops, data structures, etc.), and the Python programming environment. The second half of this course will be a getting started guide for data analysis in Python including data manipulation and cleaning techniques. By the end of this course, students will be able to take tabular data, read it, clean it, manipulate it, and run basic statistical analyses using Python. Sample Syllabus
Credits: 3
DSE 503 Systems Foundations for Data Science and Engineering
This course provides an introduction and overview of the underlying building blocks of big data stack architecture and infrastructure. It covers the foundational concepts and techniques of data acquisition, data storage, high-performance computing, and parallel data analysis. It provides hands-on experiments using advanced computing platforms and modern software tools to perform parallel data-intensive computing. Sample Syllabus
Prerequisites and notes: SIE 507 or equivalent or permission
Credits: 3
DSE510 Data Science Practicum
The Data Science Practicum introduces students to standard tools and methods used to explore, visualize, and analyze data. Students will become familiar with preprocessing and data cleaning, effective visualization methods and their application as pertinent to different data types and basic data analysis. Students will gain knowledge and experience through applying data science tools and methods to real world data sets. The course will be taught using Python.
Prerequisites and notes: grad program admission and SIE507 or permission
Credits: 3
DSE 589 – Graduate Project
Directed study on a particular spatial information science topic and implementation of a related project. Syllabus: developed by adviser and student
Prerequisites & Notes: DSE Master Project Students.
Credits: 3
DSE 590 – Data Science and Engineering Internship
Utilization of knowledge gained from the information systems graduate program within a business, non-profit or government organization and acquisition of practical training.
Prerequisites & Notes: Successful completion of nine credits of required courses in a school graduate program. Student needs to acquire permission to enroll in Internship course by filling out this form and submitting it to the Graduate Coordinator in the semester prior to the internship. (DSE590 Internship Form)
DSE 699 – Graduate Thesis/Research
Graduate thesis or research conducted under the supervision of student’s advisor.
Credits: arranged
Supplemental Note: thesis credits may alternatively be acquired in the home program of the major advisor
ECE Graduate Course Descriptions
ECE 515 – Random Variables and Stochastic Processes
Engineering applications of probability theory. Analysis of random variables, random processes and stochastic models. Introduction to the analysis and optimization of linear systems with random inputs.(Fall.)
Prerequisites & Notes: graduate standing, MAT 332 or equivalent.
Credits: 3
ECE 523 – Mathematical Methods in Electrical Engineering
Application of mathematical and numerical methods to Electrical Engineering problems. Topics include: systems of linear equations, sparse matrices, nonlinear equations, optimization, interpolation, numerical integration and differentiation, ordinary differential equations, error analysis, application to linear and nonlinear circuit analysis.
Prerequisites & Notes: Senior or graduate standing in ECE
Credits: 3
ECE 533 – Advanced Robotics
Introduces intelligent robot control system and programming. Robot dynamical equations, path planning and trajectory generation, control system, off-line simulations, robot languages and vision integration in robot applications will be discussed. Lec 2, Lab 3.