DSE Program Course Descriptions


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)

Credits: 3

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. (Spring.)
Prerequisites & Notes: ECE 417
Credits: 3
Supplemental Note: currently offered biannually every spring in even numbered years

ECE 571 – Advanced Microprocessor-Based Design
Includes techniques for developing software and hardware for microprocessor-based systems, computer aided design using a multistation logic development system, use of components commonly found in microprocessor-based systems. Lec 2, Lab 3. (Spring.)
Prerequisites & Notes: ECE 471 or permission.
Credits: 3

ECE 573 – Microprogramming
Fundamentals of microcoding and the design of microcoded systems including bit slice design. Lec 2, Lab 3. (Fall.)
Prerequisites & Notes: ECE 471, ECE 475.
Credits: 3

ECE 574 – Cluster Computing
Advances in high-end computational technology continue to bring the digital revolution into academic, industrial and commercial areas. A popular approach for achieving high performance for these application domains is to use parallel computers. Introduces the primary parallel computer architectures, as well as the programming techniques applicable to concurrent, parallel and distributed computations. Students will gain experience in developing parallel computing solutions for challenging problems. Lec 3.
Prerequisites & Notes: At least a C- in ECE 177 or permission.
Credits: 3
Supplemental Note: normally offered biannually in Spring

ECE 577 – Fuzzy Logic
This course covers the fundamentals of fuzzy logic and its application in control, model identification, information systems, and pattern recognition, as well as in conjunction with artificial neural networks and genetic algorithms.
Prerequisites & Notes: ECE 477 or permission.
Credits: 3

ECE 583 – Coding Theory
In this course students will learn how to compute the maximum rate of reliable transmission and design, evaluate, and implement codes that achieve capacity with reasonable decoding complexity.
Prerequisites & Notes: ECE 515 or permission.
Credits: 3

ECE 584 – Estimation Theory
This graduate level course is designed to follow the stochastic processes course. The main goal of this course is to help students understand concepts of estimation theory with specific focus on stochastic prediction. Both theoretical and practical aspects of estimation theory are covered. Topics include: modeling linear dynamic systems, linear prediction and filtering, implementation issues, non-linear prediction, and diagnostics statistics. Computer simulation will be used to implement the theories and solve real world problems such as navigation using sensors.
Prerequisites & Notes: ECE 515 or Instructor’s Permission. Students are required to have knowledge of probability theory and advanced statistics.
Credits: 3

ECE 585 – Fundamentals of Wireless Communication
Aims to present the modern wireless communication concepts in a coherent and unified manner and to illustrate the concepts in the broader context of the wireless systems on which they have been applied. Recent wireless standards will be studies in depth and emphasized through a course project.
Prerequisites & Notes: CHB 350 or ECE 316 or ECE 515 or MAT 332 or instructor permission.
Credits: 3

ECE 590 – Neural Networks
Introduces artificial neural networks. Provides supervised and unsupervised learning in single and multi-layer networks, software implementation, hardware overview. Applications in pattern recognition and image analysis. (Fall.)
Prerequisites & Notes: permission.
Credits: 3

ECE 598 Deep Learning (proposed permanently as ECE 591)
This course is an introduction to deep learning. Topics include convolution neural networks, recurrent neural networks, generative networks, and deep learning for scientific applications. Python will be used in this course. Students will gain hand-on experiences of developing deep learning programs to solve real problems. Sample Syllabus
Prerequisites & Notes: ECE 177 or COS 220 or instructor’s permission.
Credits: 3

ECO Graduate Course Descriptions

ECO 530 – Econometrics
Quantitative analysis of structural economic models, forecasting and policy analysis: statistical inference and data analysis, general linear statistical model specification, estimation, and hypothesis testing, univariate time-series analysis, and estimation and use of simultaneous equation models. Practical application of econometric models through computer exercises.
Prerequisites & Notes: MAT 126 and MAT 215/MAT 232, or permission.
Credits: 3

ECO 531 – Advanced Econometrics and Applications
Econometric models and techniques used in applied research: spatial data; panel data; nonlinear estimation; qualitative dependent variables; and limited dependent variables. Second of a two course sequence.
Prerequisites & Notes: A “B” or better in ECO 530 or permission.
Credits: 3

ECO 532 – Applied Time Series Econometrics
This is a graduate course in applied time series econometrics. Theorems and proofs will not be emphasized in this course. Instead, we will work to develop both a significant understanding of the role of time series econometrics in empirical econometrics and a strong ability to execute applied time series econometrics in the development of economic models and in the analysis of economic policy. Identification, estimation, evaluation, hypothesis testing, forecasting, and simulation will be emphasized. Both univariate and multivariate time series processes will be covered and applications will include both microeconomic and macroeconomic models. Sample Syllabus
Prerequisites & Notes: ECO 530, or instructor permission.
Credits: 3
Supplemental Note: currently offered biannually every spring in even numbered years

EHD Graduate Course Descriptions

EHD 572 – Advanced Qualitative Research
Designed for advanced graduate students, this course examines theoretical foundations, methodologies, methods, analysis, interpretation, and writing in qualitative inquiry with an emphasis in education. In-depth fieldwork is a core component of the course.
Prerequisites & Notes: EHD 571 or equivalent course.
Credits: 3

EHD 573 – Statistical Methods in Education I
Introduction to descriptive and inferential statistics as applied to education and human behavior. Emphasis on parametric statistics.
Prerequisites & Notes: none
Credits: 3

EHD 574 – Statistical Methods in Education II
Builds on the statistical foundation provided in EDS 521. Topics include power analysis, factorial and repeated-measures analysis of variance, multiple regression and factor analysis. Students use statistical software for data analysis.
Prerequisites & Notes: EHD 573 or equivalent.
Credits: 3

GEO Graduate Course Descriptions (Univ of Southern Maine)

GEO 605 Remote Sensing
Theory and techniques of image processing and analysis for remotely sensed digital data acquired from airplane and satellite platforms. Topics include image enhancement and classifications, spectral analysis, and landscape change detection techniques. Practical applications of natural and built landscapes are considered using remotely sensed datasets and techniques. Prerequisite: graduate standing. Credits: 3 

GIS Graduate Course Descriptions (University of Maine at Machias)

 ANT 521 Geographic Information Systems I
Students will build an understanding of the fundamentals of a GIS through lecture, readings, and computer activities. Students will learn to use a specific GIS software system, ArcGIS, and to define and complete a simple GIS project using existing data. This computer-intensive course includes a detailed discussion and related computer activities on the following topics: basic geography and map concepts, what a GIS is, data sources, data quality, databases, data classification, vector and raster data, spatial analysis, project management, cartographic communication, projections, datums, coordinates, and ethics. Sample Syllabus
Prerequisites & Notes:
Instructor permission
Credits: 3

ANT 522 Geographic Information Systems II
This is an intermediate/advanced course for students who have had some introduction to GIS and wish to pursue applications in the natural and social sciences. We will focus on grid-based data models for visualization, modeling, and analysis. Assessment will be based on problem sets, lab work, and a final project. Readings, assignments, activities, and discussions will cover: The raster data model, generating and working with grid data, georeferencing images and grids, remote sensing technologies and data, visualizing and managing raster data sets, interpolation methods for generating continuous surface data, mathematical operations with grid data for spatial analysis with satellite imagery, evaluating and documenting error and uncertainty, ethics and accountability in spatial analysis, modeling and visualization. Sample Syllabus
Prerequisites & Notes: ANT 521 or instructor permission.
Credits: 3

GIS 420 Remote Sensing & Image Analysis
Earth imaging from satellites, aircraft and remote sensors is increasingly crucial to visualizing and analyzing environmental change. This course introduces remote sensing technologies used in mapping, with an emphasis on satellite imagery and lidar. Using industry-standard software and imagery, students learn basic image analysis for oceanographic modeling, land cover change detection, climate analysis and similar applications. The course combines lecture, discussion and mapping exercises to cover the major remote sensing technologies and image formats, the physics of light and optics, potential sources of error, analytical methods and applications of remote sensing in a variety of fields. The semester culminates in a final project. Sample Syllabus
Prerequisites & Notes: ANT 522 or instructor permission.
Credits: 3
Supplemental Note: Normally offered biannually in Fall in even years. A maximum of two 400-level courses may be contained on a graduate program of study

GIS 426 Community Applications of GIS
Students work together under the instructor’s guidance for a single community client to perform a professional-quality service project using geographic information systems (GIS) as a decision-support and planning tool. Projects might include a town’s comprehensive plan, environmental conservation planning, economic development, recreation planning, emergency response management or similar applications where GIS can assist communities in setting priorities, making choices or planning for the future. Students will be expected to work closely with clients and/or community residents to assess and respond to their needs, answer questions and provide them with maps, data and documentation. In most cases, students will present their findings to the clients or their constituents. Through this work, students learn to plan, manage, execute and document a multi-faceted GIS project, skills with direct applications to the workforce. Sample Syllabus
Prerequisites & Notes: ANT 522 or instructor permission.
Credits: 3.
Supplemental Note: Normally offered biannually in Fall in odd years. A maximum of two 400-level courses may be contained on a graduate program of study. 

GIS 428 – Web-Based Maps, Applications & Services
This is a practical and applied course covering design and delivery of web and mobile maps and applications, fundamentals of online databases, hosting and serving data and map services and basics of server management. The course will cover a variety of software and server providers, including Esri, Google and open source, focusing mainly on those with the greatest market share and practical value in the workplace. Students will work with services and cloud services in the course, which culminates in a real-world service project.
Prerequisites & Notes: ANT 521 and ANT 522 or instructor permission.
Credits: 3
Supplemental Note: Normally Offered biannually in Spring in odd years. A maximum of two 400-level courses may be contained on a graduate program of study

HTY Graduate Course Descriptions

HTY 665 – Digital and Spatial History
The digital revolution has transformed historical scholarship and teaching by enabling access to a wealth of research material and instructional resources. Many historians, however, have been hesitant to adopt digital methods of empirical analysis. This seminar will examine the challenges and opportunities of digital scholarship, including how digital methods affect the process of research, the questions historians ask, the sources they use, and the answers they find. We will particularly consider spatial history, where GIS (geographic information systems), digital mapping, and other visual approaches to data analysis and representation push the boundaries of traditionally text-centric narrative history. Over-arching themes of the course are the costs and benefits of digital methods and the impact of methodological choices on historical research. This course can be taken remotely through teleconferencing.
Prerequisites & Notes: Graduate standing, or permission of the instructor for qualified undergraduate seniors.
Credits: 3
Supplemental Note: normally offered every third year

INT Graduate Course Descriptions

INT 527 – Integration of GIS and Remote Sensing Data Analysis in Natural Resource Applications
Analysis of satellite imagery and GIS data bases including applications of raster and vector models, land cover analysis and forest change detection, wildlife habitat analysis, hydrological assessment, and landscape characterization. Sample Syllabus
Prerequisites & Notes: permission of instructor; senior or graduate standing.
Credits: 3

INT 601 – Responsible Conduct of Research
Key topics in conducting research responsibly. Guidelines, policies and codes relating to ethical research. Skills development for identifying and resolving ethical conflicts arising in research. Address case studies in the context of ethical theories and concepts.
Prerequisites & Notes: graduate standing
Credits: 1

PSE Graduate Course Descriptions

PSE 509 – Experimental Design
Principles of research in biological sciences, design of experiments, statistical analysis and interpretation of data. Lec 3, Lab 2.
Credits: 4

PSY Graduate Course Descriptions

PSY 540 – Advanced Psychological Statistics and Methods I
A two semester advanced-level course. Topics include control, reliability of measurement, and validity in relation to both experimental and non-experimental approaches.
Prerequisites & Notes: PSY 241 or equivalent
Credits: 3

PSY 541 – Advanced Psychological Statistics and Methods II
A two semester advanced-level course. Topics include control, reliability of measurement, and validity in relation to both experimental and non-experimental approaches.
Prerequisites & Notes: PSY 241 or equivalent
Credits: 3

SIE Graduate Course Descriptions

SIE 501 – Introduction to Graduate Research
Covers process of successful graduate research from identification of a researchable question, preparation of a thesis proposal, to completion or the research and its publication. Focus on engineering research methods for spatial information. Example Past Syllabus
Prerequisites & Notes: None
Credits: 1

SIE 502 – Research Methods
Covers process of successful graduate research, including the written and verbal presentation of plans and results. Students formulate hypotheses, perform a literature search, write abstracts and introductions of research papers, learn about presentation styles and techniques, make two presentations (3-minutes and 10-minutes) about research proposals. Example Past Syllabus
Prerequisites & Notes: SIE 501 and students must have selected a thesis topic.
Credits: 1

SIE 505 – Formal Foundations for Information Science
Increases student’s understanding of the approach to information systems and science by formalisms. Draws on mathematics to increase familiarity with formal syntax and language, develops understanding and technical ability in handling structures relevant to information systems and science. Includes a review of fundamental material on set theory, functions and relations, graph theory, and logic; examines a variety of algebraic structures; discusses formal languages and the bases of computation. Example Past Syllabus
Prerequisites & Notes: SIE 550 or instructor permission
Credits: 3

SIE 508 – Object Oriented Programming
Object-oriented programming represents the integration of software components into large-scale software architecture. This course introduces advanced programming skills and focuses on programming and design using a high-level object-oriented language, either Python or Java. The core concepts of object-oriented programming are examined and practical applications in the domain of data science and as seen in stacks, queues, lists, and trees are explored. Example Past Syllabus
Prerequisites & Notes: SIE 507 or permission of instructor.
Credits: 3

SIE 509 – Principles of Geographic Information Systems
Covers foundation principles of geographic information systems, including traditional representations of spatial data and techniques for analyzing spatial data in digital form. Combines an overview of general principles associated with implementation of geographic information systems and practical experience in the analysis of geographic information. Example Past Syllabus
Prerequisites & Notes: Graduate standing or instructor permission.
Credits: 3

SIE 510 – Geographic Information Systems Applications
Introduces both conceptual and practical aspects of developing GIS applications. Covers application areas from natural resource planning through transportation, cadastral and land information systems and their spatial modeling requirements, and application development from requirement analysis to database design and implementation. Example Past Syllabus
Prerequisites & Notes: SIE 509 or permission.
Credits: 3

SIE 512 – Spatial Analysis
Introduces students to techniques for spatial analysis. Covers methods and problems in spatial data sampling, issues in preliminary or exploratory analysis, problems in providing numerical summaries and characterizing spatial properties of map data and analysis techniques for univariate and multivariate data. Students will be responsible for completing several hands-on exercises. Example Past Syllabus
Prerequisites & Notes: An introductory statistics course and graduate standing or instructor permission.
Credits: 3

SIE 515 – Human Computer Interaction
Students are introduced to the fundamental theories and concepts of human-computer interaction (HCI). Topics covered include: interface design and evaluation, usability and universal design, multimodal interfaces (touch, gesture, natural language), virtual reality, and spatial displays. Example Past Syllabus
Prerequisites & Notes: None
Credits: 3

SIE 516 – Interactive Technologies for Solving Real-World Problems
This course is designed to provide students with an overview of the basic principles of interactive design and immersive technology (virtual, augmented, mixed, and extended reality). The goal is to learn enough about the strengths and limitations of this technology, and the associated human factors, to design simple prototypes aimed at solving real-world problems. Example Past Syllabus
Prerequisites & Notes: Programming experience and graduate standing or instructor permission
Credits: 3

SIE 517 – Spatial Interaction Design (SIE 598 currently)
The main objective of this course is to provide a hands-on experience of interaction design research practice focusing on the interactive prototype construction. The principles and technologies of interaction design will be learned by adding expressive interactions to objects and spaces around us (spatial interactions). Interaction Design (IxD) discovers people’s needs, understands the context of use, frames product opportunities, and propose useful, usable, and desirable (usually digital) products. Interaction designers often work with narrative to explore and refine desired behaviors and user experience. This interdisciplinary course (projects based) will engage students with the fundamentals of interaction design and applied interaction design methods to shape behavior between people and products, services, and environments. First, we will select a specific location in a domestic setting (for example, the kitchen, dining room, office space, or the playground), then discuss and develop digital interactions for novel experiences. Example Past Syllabus
Prerequisites & Notes: Program admission or instructor permission.
Credits: 3

SIE 525 – Information Systems Law
Current and emerging status of computer law in electronic environments: rights of privacy, freedom of information, confidentiality, work product protection, copyright, security, legal liability; impact of law on use of databases and spatial datasets; legal options for dealing with conflicts and adaptations of law over time. Example Past Syllabus
Prerequisites & Notes: Graduate standing or instructor permission.
Credits: 3

SIE 550 – Design of Information Systems
Cognitive and theoretical foundation for representation of knowledge in information systems and fundamental concepts necessary to design and implement information systems. Logic programming as a tool for fast design and prototyping of data models. Formal languages and formal models, conceptual modeling techniques, methods for data abstraction, object-oriented modeling and database schema design. Relational data model and database query languages, including SQL. Example Past Syllabus
Prerequisites & Notes: Graduate standing or instructor permission.
Credits: 3

SIE 554 – Spatial Reasoning
Qualitative representations of geographic space. Formalisms for topological, directional and metric relations; inference mechanisms to derive composition tables; geometric representations of natural language-like spatial predicates; formalizations of advanced cognitively motivated spatial concepts, such as image schemata; construction of relation algebras. Example Past Syllabus
Prerequisites & Notes: SIE 550.
Credits: 1 or 3

SIE 555 – Spatial Database Systems
Covers internal system aspects of spatial database systems. Layered database architecture. Physical data independence. Spatial data models. Storage hierarchy. File organization. Spatial index structures. Spatial query processing and optimization. Transaction management and crash recovery. Commercial spatial database systems.  Example Past Syllabus
Prerequisites & Notes: SIE 550 and programming experience in Java, C++ or C.
Credits: 3

SIE 557 – Database System Applications
Study, design and implementation of object-relational database system applications. Introduction to database systems. Integrating database systems with programs. Web applications using database systems. Final database project. Example Past Syllabus
Prerequisites & Notes: SIE 507.
Credits: 3

SIE 558 – Real-time Sensor Data Streams
This course is an introduction into the technology of sensor data stream management. This data management technology is driven by computing through sensors and other smart devices that are embedded in the environment and attached to the Internet, constantly streaming sensed information. With streams everywhere, Data Stream Engines (DSE) have emerged aiming to provide generic software technology similar to that of database systems for analyzing streaming data with simple queries in real-time. Sensor streams are ultimately stored in databases and analyzed using scalable cloud technologies.  Example Past Syllabus
Prerequisites & Notes: Graduate standing, programming experience in Java, C++, or C, or permission of the instructor.
Credits: 3

SIE 559 – Geosensor Networks
Readily available technology of ubiquitous wireless communication networks, the miniaturization of computing and storage platforms as well as the development of novel microsensors and sensor materials has lead to the technology of wireless geosensor networks (GSN). Geosensor networks have changed the type of dynamic environmental phenomena that can be detected, monitored and reacted to, often in real-time. In this course, we will survey the field of wireless geosensor networks, and explore the state of the art in technology and algorithms to achieve energy-efficient, robust and decentralized spatial computing. Example Past Syllabus
Prerequisites & Notes: Graduate standing, programming experience in Java, C++, or C, or permission of the instructor.
Credits: 3.

SIE 580 – Ontology Engineering Theory and Practice (proposed permanently as SIE 585)
Ontologies are explicit specifications of information models and their semantics in formats that are interpretable by humans and computers. The course introduces the philosophical and logical foundations of ontologies and surveys formalisms, modern languages and methods for designing, analyzing and using ontologies. The stages of ontology development from conceptual design to ontology evaluation and verification are studied and practiced using concrete domains.
Example Past Syllabus
Prerequisites & Notes: SIE 505 or instructor permission.
Credits: 3
Supplemental Note: normally offered biannually in Fall. Course number changing to SIE 585.

SIE 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: SIE Master Project Students.
Credits: 3

SIE 590 – Information Systems Internship
Utilization of knowledge gained from the information systems graduate program within a business, non-profit or government organization and acquisition of practical training. Syllabus with Forms
Prerequisites & Notes: Successful completion of nine credits of required courses in a school graduate program.
Credits: 3

SFR Graduate Course Descriptions

SFR 528 – Qualitative Data Analysis in Natural Resources
Principles and practices of qualitative data analysis in natural resources. The course covers various interpretive analytical traditions in the social sciences, as well as strategies used in qualitative data analysis. Students will analyze previously collected qualitative data, and develop a written document that includes both narrative and visual displays. The course includes hands-on NVivo training on coding data.
Prerequisites & Notes: EHD 571 or permission.
Credits: 3 
Supplemental Note: normally offered biannually in Fall in even years.

SFR 609 – Remote Sensing Problems
Credits: Arranged

SMS Graduate Course Descriptions

SMS 540 – Satellite Oceanography
An overview of the use of remote sensing technologies for making measurements of the marine environment. Introduces the various sensors used by oceanographers, their background, the principles behind their operation and measurement retrieval. Emphasis will be placed on readings from the prime oceanography literature and biogeophysical applications of the data, their analysis, advantages and limitations rather than physical/optical theory.
Prerequisites & Notes: SMS 501 and SMS 541 or permission.
Credits: 3
Supplemental Note: offered every third or fourth year in Fall. Contact instructor.

SMS 595 – Data Analysis Methods in Marine Sciences
Provides theoretical and computational guidance on techniques commonly used in data analysis. The first half of the course will cover regression methods and the second half will cover time series analysis and digital filters. Real data will be used to illustrate the practical aspects of the subject with emphasis on developing a hands-on understanding of the methods and correct interpretation of results.
Prerequisites & Notes: MAT 126 or equivalent.
Credits: 3

STS Graduate Course Descriptions

STS 437 – Statistical Methods in Research
An introduction to analysis of variance and regression analysis using a unifying approach to theory; application and illustrations from many fields.
Prerequisites & Notes: A grade of C or better in STS 232 or STS 434 or Department permission. Course Typically Offered: Fall
Credits: 3

STS 531 – Mathematical Statistics I
Covers axioms of probability, random variables, continuous and discrete distributions, moment generating functions, distributions of functions of random variables, sampling distributions.
Prerequisites & Notes: A grade of C or better in MAT 425, STS 434 or permission.
Credits: 3
Supplemental Note: normally offered biannually

STS 533 – Stochastic Systems
The study of mathematical models which involve random processes. Topics include Poisson process, waiting-line models, Markov chains, decision analysis and reliability theory. Some emphasis on modeling problems encountered in business and industry.
Prerequisites & Notes: A grade of C or better in STS 434.
Credits: 3
Supplemental Note: normally offered biannually

SVT Graduate Course Descriptions

SVT 437 – Practical GPS
Presentation of all types of GPS equipment with their uses and limitations, GPS observation planning based on satellite geometry and obstructions, review of geodetic coordinate systems and datums, the geoid and how it relates to the production of elevations from GPS, execution of all components (planning, field collection, downloading, processing, and adjustment) of a GPS survey where raw data is collected, real time kinematic (RTK) GPS filed execution and adjustment for control work, use of RTK GPS in collection of a topographic survey. Lec 2, Lab 2. Sample Syllabus
Prerequisites & Notes: SVT 341 or equivalent. Course Typically Offered: Fall
Credits: 3

SVT 531 – Advanced Digital Photogrammetry
Airborne GPS-IMU processing techniques; conversion between local cartesian and conventional mapping coordinate systems; techniques in automated pixel matching; digital cameras and their calibration; optimization of automated photocoordinate measurement for aerotriangulation; recursive partitioning techniques for aerotriangulation solution optimization; techniques for automated feature extraction; synthesis of digital imagery and Lidar; image enhancements issues in orthophotos and mosaics; multi-ray considerations. Sample Syllabus
Prerequisites & Notes: Prior coursework in photogrammetry/remote sensing.
Credits: 3

SVT 532 – Survey Strategies in Use of Lidar
Types of Lidar sensors and their applications; integration of GPS-IMU with Lidar; calibration; elimination of non-ground data; break line extraction; ground based mobile Lidar; Integration of survey control into Lidar data sets; accuracy assessment of overlapping scanned data; the industry standard .las format; integration with other survey information; Lidargrammetry; classifying Lidar data by return number and layer; procedures for geodetic accuracy assessment; corridor mapping. Sample Syllabus
Prerequisites & Notes: none listed
Credits: 3