ABE 4008 Control Methods in SmartAg Systems 3 Credits
Grading Scheme: Letter Grade
Design, analysis, simulation, and programming modern control methods for applications in production agriculture, biological and food engineering, land and water resources. Learn theoretical concepts, application programming, and simulation techniques using classical and modern control approaches, fuzzy logic, neural networks, and other intelligent learning algorithms.
Prerequisite: MAP 2302 and PHY 2048;
Corequisite: EGM 3400.
Attributes: Artificial Intelligence
ABE 4641 Modeling Coupled Natural-Human Systems 3 Credits
Grading Scheme: Letter Grade
Explore approaches to modeling coupled natural-human systems, drawing from both natural and social sciences. Topics include regime shift from dynamical systems and basic concepts from game theory and social-ecological system literature. These are combined in models that operationalize a conceptual framework. With guidance, develop models for final projects.
Prerequisite: MAC 2312 or equivalent.
Attributes: Artificial Intelligence
ABE 4662 Quantification of Biological Processes 3 Credits
Grading Scheme: Letter Grade
Quantitative description and analysis of biological processes pertaining to microbes, plants, animals and ecosystems. Biological transport phenomena, bioenergetics, enzyme kinetics, metabolism, bioregulation, circulatory and muscle systems, agroecosystems. Analytical and experimental laboratory for development of quantitative skills.
Prerequisite: (ABE 2062 or BSC 2010) and (EGN 3353C or CWR 3201).
Attributes: Artificial Intelligence
ADV 3330 Artificial Intelligence and Advertising 3 Credits
Grading Scheme: Letter Grade
Provides a comprehensive understanding of AI and its impact on the advertising industry. Explore a range of AI-driven tools, techniques, and technologies while examining their practical applications in marketing and advertising campaigns. Additionally, covers the ethical and societal implications of AI technology in human communication and marketing.
Prerequisite: ADV 3008 & MAR 3023 with minimum grades of C & Advertising major.
Attributes: Artificial Intelligence
ADV 3500 Digital Insights 3 Credits
Grading Scheme: Letter Grade
Acquiring, evaluating, and analyzing information for advertising decisions. Emphasizes understanding the scientific method, developing explicit and measurable research objectives, selecting appropriate methodologies, and analyzing data.
Prerequisite: MAR 3023 and ADV 3008 with minimum grades of C and STA 2023 and ADV major.
Attributes: Artificial Intelligence, Enable-AI
ADV 4300 Media Planning 3 Credits
Grading Scheme: Letter Grade
Provides an in-depth overview of the media planning process. Emphasizes the value of various media channels and evaluation methods to design innovative and integrated media strategies to reach and engage diverse audiences.
Prerequisite: 3JM ADV; minimum grades of C in ADV 3001 and ADV 3500.
Attributes: Artificial Intelligence
ADV 4331 AI-Driven Social Media Insight 3 Credits
Grading Scheme: Letter Grade
For those intrigued by social media campaigns, including influencer marketing and societal initiatives, irrespective of their programming background. It melds the theoretical underpinnings of social media analytics with hands-on experience in Python and supplementary software tools, presented through lectures, workshops, and interactive discussions.
Prerequisite: ADV 3008 & MAR 3023 with minimum grades of C & Advertising major of junior standing or higher
Attributes: Artificial Intelligence, Build-AI
AEB 4325 Contemporary Issues in Agribusiness Management 3 Credits
Grading Scheme: Letter Grade
A capstone course utilizing economic concepts to address the interaction between the political process that legislates domestic agricultural, environmental and international trade policy, micro and macro economic principles, private business decisions taken by firms in response to public policies, and ethical considerations in developing and implementing public policy.
Corequisite: AEB 4138 or AEB 4342.
Attributes: Artificial Intelligence
AEC 3033C Research and Business Writing in Agricultural and Life Sciences 3 Credits
Grading Scheme: Letter Grade
Establishes the importance of effective communication to success in both the educational and professional environments; emphasizes writing as a primary form of communication; examines the elements of effective written communication in organizational and scholarly areas; and explores the causes of ineffective writing and ways to correct them. (WR)
Prerequisite: Junior standing or higher.
Attributes: Enable-AI, Satisfies 6000 Words of Writing Requirement
AEC 3073 Intercultural Communication 3 Credits
Grading Scheme: Letter Grade
Basic culturally coded communication behaviors, such as cultural values and beliefs, attitudes and verbal and non-verbal behavior, are examined to identify basic differences among individuals from diverse cultural backgrounds. Special emphasis on cultural communication issues in the agricultural and natural resources sciences are addressed.
Prerequisite: Sophomore standing or higher.
Attributes: Enable-AI, General Education - International
ALS 3200C AI in Agricultural and Life Sciences 3 Credits
Grading Scheme: Letter Grade
Artificial intelligence (AI) is used to solve problems in research and industry. Provides an understanding of and practical, hands-on experience building and using AI systems. Gain the skills and knowledge needed to use AI to solve real-world problems in agricultural and life sciences.
Prerequisite: (BSC 2891 or STA 2023 or STA 3032 or EEL 3872) with minimum grades of C.
Attributes: Artificial Intelligence
ANT 4180L Laboratory Training in Archaeology 1-3 Credits
Grading Scheme: Letter Grade
Processing of data recovered in field excavations; includes cleaning, identification, cataloguing, classification, drawing, analysis, responsibilities of data reporting.
Prerequisite: an introductory-level archeology course.
Attributes: Artificial Intelligence
AOM 4434 Precision Agriculture 3 Credits
Grading Scheme: Letter Grade
Principles and applications of technologies supporting precision farming and planning for natural resource data management. Global positioning systems (GPS), geographic information systems (GIS), variable rate technologies (VRT), data layering of independent variables, automated guidance, Internet, information access and computer software for management.
Prerequisite: Junior standing or higher.
Attributes: Enable-AI
AOM 4455 Agricultural Operations and Systems 3 Credits
Grading Scheme: Letter Grade
Quantitative and managerial techniques for management and planning of technical resources in agriculture. Applications of queuing theory, project scheduling, optimization, and expert decision systems.
Prerequisite: ((MAC 1147) or (MAC 1114 & MAC 1140) or (MAC 2233)) & CGS 2531.
Attributes: Artificial Intelligence
APK 4720 Artificial Intelligence for Movement Sciences 3 Credits
Grading Scheme: Letter Grade
Apply AI technologies to the study of movement in healthy and diseased human populations. Utilize programming and data visualization while covering classical concepts of machine learning (e.g. linear regression and classification, ensemble-based algorithm, clustering, and neural networks) and modern machine learning methods for images and video processing (e.g. convolutional neural networks and transformers).
Prerequisite: (MAC 1147 or MAC 2311 or STA 2023 or APK 3220C or BME 3219 (or obtain instructor permission) or EEL 3872) and PHI 3681.
Attributes: Artificial Intelligence
APK 4721 AI in Action: Problem Solving in Health, Fitness and Human Performance 3 Credits
Grading Scheme: Letter Grade
In this classroom-based undergraduate research experience (CURE) course, students will collaborate on a research project addressing real-world challenges found in the industry-related roles in Applied Physiology and Kinesiology. Using AI to develop innovative solutions, students will gain practical experience and industry-relevant skills for careers in health, fitness, and human performance fields.
Prerequisite: APK4050 Research Methods.
Attributes: Artificial Intelligence
ART 3959C Video Art 3 Credits
Grading Scheme: Letter Grade
Explores video with an emphasis on editing and building a personal vocabulary through the electronic image.
Prerequisite: ART 2680C and must be a (BFA Art or BA Art or BFA Graphic Design major) and must have passed sophomore portfolio review.
Attributes: Artificial Intelligence
ART 4612C Digital Media Workshop 3 Credits
Grading Scheme: Letter Grade
Bridges the study of digital media and broadly envisioned professional practices in the field. Emphasis on portfolio and project development for transition to advanced study or professional, expressive or applied practices in integrated media.
Prerequisite: Must be BFA Art or BA Art or BFA Graphic Design major and must have passed sophomore portfolio review.
Attributes: Artificial Intelligence
ART 4645C Sensors and Electronics-Based Art 3 Credits
Grading Scheme: Letter Grade
Physical computing HCI (human computer interaction) explores how devices respond to and interact with human physical action. Students will create artwork that explores physical interfaces beyond mouse/keyboard/screen interactions through the use of microcontrollers and sensors.
Prerequisite: Must be BFA Art or BA Art or BFA Graphic Design major and must have passed sophomore portfolio review.
Attributes: Artificial Intelligence
AST 2730 Introduction to Python for Physical Sciences 4 Credits
Grading Scheme: Letter Grade
Learn syntax, capabilities, and foundations of Python and basic numerical methods to address physical problems with a computational approach. Covers basics of dataset manipulation, algorithm development, and plotting.
Attributes: Artificial Intelligence
BCN 3611C Construction Estimating 1 3 Credits
Grading Scheme: Letter Grade
Classification of work, quantity survey techniques and basic estimating principles applied to simple construction projects.
Prerequisite: BCN 3027C and BCN 3224C and BCN 3255C.
Attributes: Artificial Intelligence
BCN 4594 Building Energy Modeling 3 Credits
Grading Scheme: Letter Grade
As energy becomes a more precious commodity, it is crucial to design and operate high performance buildings. A solid foundation of energy engineering and sustainability principles is essential to achieving these higher performance standards.
Prerequisite: junior standing or higher.
Attributes: Artificial Intelligence
BME 3053C Computer Applications for BME 2 Credits
Grading Scheme: Letter Grade
Computer programming lab and lecture utilizes Matlab to analyze biomedical measurements.
Prerequisite: COP 2271 and COP 2271L or equivalent and MAC 2312, with minimum grades of C.
Attributes: Artificial Intelligence
BME 4760 Biomedical Data Science 3 Credits
Grading Scheme: Letter Grade
Covers the biomedical applications of data science techniques, which include pre-processing techniques, machine learning data analysis, and data visualization techniques.
Prerequisite: BME 3053C and COP 2271 and COP 2271L and (STA 2023 or STA 3032).
Attributes: Artificial Intelligence
BOT 2710C Practical Plant Taxonomy 3 Credits
Grading Scheme: Letter Grade
Introduces plant taxonomy including principles of systematic botany, nomenclature and classification, but emphasizing identification. Student will be able to identify the common ferns, fern allies, gymnosperms and flowering plants of field and garden.
Attributes: Artificial Intelligence
BSC 2460 Can we Design “Better” Humans? Should We? 3 Credits
Grading Scheme: Letter Grade
Introduces the topics of human cloning and human genetic modifications through analysis of international scientific data. Discusses ethical considerations of these topics.
Prerequisite: Any Quest 1 course with a minimum grade of C.
Attributes: Quest 2, Artificial Intelligence, Enable-AI, General Education - International
BSC 4452 Computational Tools for Research in Biology 3 Credits
Grading Scheme: Letter Grade
Introduces computational tools for research: Linux command line, Python scripting, databases. Prepares students to conduct large-scale data analysis on high performance computing resources.
Prerequisite: Junior standing or higher.
Attributes: Artificial Intelligence
BSC 4892 AI in Biology 3 Credits
Grading Scheme: Letter Grade
Examines how AI has rapidly become ubiquitous in daily life and been applied to diverse areas of Biology. Focuses on machine learning approaches as well as deep learning methods, including transformers. Covers machine learning methods for tabular data, computer vision, transfer learning, natural language processing, and transformer-based architectures. Classes typically applied coding with Jupyter Notebooks on HiPerGator. Prior Python coding experience required.
Prerequisite: BSC 4452 or BSC 6451 or BSC 2891 or Instructor permission (Python programming experience.)
Attributes: Artificial Intelligence
CAI 4104 Machine Learning Engineering 3 Credits
Grading Scheme: Letter Grade
Covers foundational machine learning concepts with an emphasis on applying these concepts to real-world data through programming exercises and assignments using the relevant tools, libraries, and frameworks such as Python, Scikit-Learn, Tensorflow, and more.
Prerequisite: COP 3530. Experience with Python is a plus but not required.
Attributes: Artificial Intelligence
CAP 3032 Interactive Modeling and Animation 1 3 Credits
Grading Scheme: Letter Grade
Introduces programming and data structures for interactive two-dimensional multimedia applications. Representing form and transforms in two dimensions, capturing user actions and driving application behavior interactively. Graphical interfaces, image processing, automata and basic artificial intelligence.
Prerequisite: MAC 1147 or equivalent.
Attributes: Artificial Intelligence, Enable-AI
CAP 4613 Deep Learning for Computer Graphics 3 Credits
Grading Scheme: Letter Grade
This undergraduate course covers deep learning basics, related math and the fundamental theory and application of AI algorithms most popular in the field of computer graphics. Programming assignments will help students develop GPU programming skills while implementing concepts learned in lectures and readings using deep learning APIs on a GPU cluster. Convolutional neural networks (CNNs) for colorizing black and white movies is an example.
Prerequisite: COP 3530 or MAS 3114 or 4105.
Attributes: Artificial Intelligence
CAP 4641 Natural Language Processing 3 Credits
Grading Scheme: Letter Grade
Introduction to the essential concepts, principles, and techniques of Natural Language Processing (NLP). Practical application and theoretical concepts are examined. Topics include information extraction, language construction, grammars, disambiguation, as well as system modeling, classification, and evaluation.
Prerequisite: COP 3530.
Attributes: Artificial Intelligence
CAP 4770 Introduction to Data Science 3 Credits
Grading Scheme: Letter Grade
Introduces the basics of data science including programming for data analytics, file management, relational databases, classification, clustering, and regression; lays the foundation for big data applications ranging from social networks to medical and business informatics.
Prerequisite: COP 3530.
Attributes: Artificial Intelligence
CCJ 3701 Research Methods in Criminology 4 Credits
Grading Scheme: Letter Grade
Advanced research design and data analysis. Study of experimental and non-experimental research designs; probability and nonprobability sampling techniques; construction of scales; and indexes and methods of bivariate and multivariate data analysis. Previous completion of an introductory course in statistics is recommended but not required.
Attributes: Artificial Intelligence
CEN 3031 Introduction to Software Engineering 3 Credits
Grading Scheme: Letter Grade
Topics include software planning, specifications, coding, testing and maintenance. Gain experience in the team approach to large system development.
Prerequisite: COP 3530.
Attributes: Artificial Intelligence
CGN 3421 Computer Methods in Civil Engineering 3 Credits
Grading Scheme: Letter Grade
Review of computer programming. Numerical methods as applied to civil engineering problems and civil engineering software.
Prerequisite: COP 2271 or COP 2273.
Attributes: Artificial Intelligence
CGN 4304 Machine Learning Applications in Civil Engineering 3 Credits
Grading Scheme: Letter Grade
Leverage machine learning state-of-the-art techniques and tools to solve Civil Engineering problems. Apply fundamentals of data analytics and machine learning techniques to real-world tasks and gain essential knowledge and programming skills (using R) in data preprocessing, feature selection, model comparison, hyperparameter tuning, and machine-learning interpretation. Includes case studies and applications for hands-on experience.
Prerequisite: CGN 3421.
Attributes: Artificial Intelligence
CGN 4404 Applied Data Science in Civil and Environmental Engineering 3 Credits
Grading Scheme: Letter Grade
Introduces the workflows of data science applications, covering the state-of-art techniques in data acquisition, data processing and management, analytics and modeling, and visualization. Critical application of data science concepts and techniques in complex socioeconomic and environmental contexts. Basics of problem formulation and major ethical considerations of applying data science in practice.
Prerequisite: CGN 3421 or ENV 3040C or equivalent.
Attributes: Artificial Intelligence, Enable-AI
CGS 2531 Problem Solving Using Computer Software 3 Credits
Grading Scheme: Letter Grade
Problem-solving introduction and thorough exploration of word processing, spreadsheet management, data analysis, graphical display of data, and multimedia presentations. The problem-solving approach also aids students in their specific majors through software applications requiring major-specific professional communication skills in written, graphical, and presentation forms. (M)
Attributes: General Education - Mathematics, Use-AI
CLA 3811 AI in Antiquity and Today 3 Credits
Grading Scheme: Letter Grade
Examines AI’s origins in ancient Greece and compares it to AI’s acceptance and use in modern society. Pairs discussion of Greek and Roman philosophical and other literary texts on the soul and identity and the boundaries between the natural and artificial with emerging societal issues related to AI, including gender, racism, and slavery.
Prerequisite: Students must be sophomore standing.
Attributes: Artificial Intelligence, General Education - International, Satisfies 4000 Words of Writing Requirement
COM 2380 Collaboration With AI for Better Communication 3 Credits
Grading Scheme: Letter Grade
Two pivotal questions in the realm of modern communication: “How can we communicate effectively in a world with AI?” and “How can AI tools be utilized for effective communication and collaboration for better social decisions?” Explore both the positive potential and the challenges posed by AI in professional/science communication, fostering a balanced and critical understanding of these tools. Emphasizes its interdisciplinary nature to explore the impact of AI on social institutions, structures, and processes, highlighting the intersection between technology and social science. Engage with key themes, principles, and methodologies used in social and behavioral sciences, applying them to understand and navigate the AI-influenced communication landscape.
Attributes: Quest 2, Artificial Intelligence, General Education - Social Science, Use-AI
COP 2271 Computer Programming for Engineers 2-3 Credits
Grading Scheme: Letter Grade
Computer programming and the use of computers to solve engineering and mathematical problems. Emphasizes applying problem solving skills; directed toward technical careers in fields employing a reasonably high degree of mathematics. The programming language used depends on the demands of the departments in the college. Several languages may be taught each semester, no more than one per section. Those required to learn a specific language must enroll in the correct section.
Prerequisite: MAC 2312 with minimum grade of C.
Attributes: Artificial Intelligence
COP 2271L Computer Programming for Engineers Laboratory 1 Credit
Grading Scheme: Letter Grade
Optional laboratory for COP 2271. Required for ISE majors.
Prerequisite: MAC 2312;
Corequisite: COP 2271.
Attributes: Artificial Intelligence
COP 2273 Python Programming for Engineers 3 Credits
Grading Scheme: Letter Grade
Introduction for those who have little experience in programming and have been looking to obtain hands-on learning experience in the Python programming language. Encourages developing problem solving and computational thinking skills in an engineering field and emphasizes a reasonably high degree of mathematics.
Prerequisite: MAC 2311 with a C grade or better.
Attributes: Artificial Intelligence
COP 2800 Computer Programming Using JAVA 3 Credits
Grading Scheme: Letter Grade
In-depth treatment of computer programming using JAVA. Problems related to a variety of disciplines are solved. Introduces the basic concepts of software and hardware; develop a variety of stand-alone applications and applets. For non-CISE majors only.
Prerequisite: MAC 1147 or the equivalent.
Attributes: Artificial Intelligence
COP 3275 Computer Programming Using C 3 Credits
Grading Scheme: Letter Grade
Solve problems related to a variety of disciplines; introduces the basic concepts of software and hardware.
Prerequisite: MAC 1147 or the equivalent.
Attributes: Artificial Intelligence
COP 3502C Programming Fundamentals 1 4 Credits
Grading Scheme: Letter Grade
First course of a two-semester introductory sequence for those planning further study in computer science, digital arts and sciences or computer engineering. Concepts of computer science and the process of computer programming, including object-oriented programming, procedural and data abstraction and program modularity.
Corequisite: MAC 2311.
Attributes: Artificial Intelligence
COP 3503C Programming Fundamentals 2 4 Credits
Grading Scheme: Letter Grade
Second course of a two-semester introductory sequence for those planning further study in computer science, digital arts and sciences or computer engineering. Concepts of computer science and the process of computer programming, including object-oriented programming, procedural and data abstraction and program modularity.
Prerequisite: COP 3502C and MAC 2311 both with minimum grades of C.
Attributes: Artificial Intelligence
COP 3504C Advanced Programming Fundamentals for CIS Majors 4 Credits
Grading Scheme: Letter Grade
Fast-paced introduction to computer science for those with prior programming experience. Explores major concepts of computer science and the process of computer programming, including object-oriented programming, procedural and data abstraction and program modularity.
Prerequisite: (MAC 2311 or MAC 3472) and programming experience.
Attributes: Artificial Intelligence
COP 3530 Data Structures and Algorithm 3 Credits
Grading Scheme: Letter Grade
Algorithm development using pseudo languages, basic program structures, program design techniques, storage and manipulation of basic data structures like arrays, stacks, queues, sorting and searching and string processing. Linked linear lists. Trees and multilinked structures.
Prerequisite: (COP 3504 or COP 3503) and COT 3100 and (MAC 2234 or MAC 2312 or MAC 2512 or MAC 3473), all with a minimum grade of C.
Attributes: Artificial Intelligence
DCP 4300 AI in the Built Environment 3 Credits
Grading Scheme: Letter Grade
Introduces Artificial Intelligence (AI) and its applications to real world problems in planning, design, and construction of the built environment. Includes application in professional practice in architecture, construction management, interior design, landscape architecture, and urban and regional planning.
Prerequisite: EEL 3872 and PHI 3681.
Attributes: Artificial Intelligence
ECO 4401 Mathematical Economics 4 Credits
Grading Scheme: Letter Grade
Introduces fundamental mathematical tools employed in economic analysis. Covers comparative static analysis, introduces linear algebra, constrained and unconstrained optimization, and dynamic analysis using differential and difference equations. Examines applications from a wide range of subfields in economics, including consumer theory, macroeconomics, economic growth, and environmental economics.
Prerequisite: (ECO 2013 and ECO 2023 and ECO 3101) or (ECO 3203 and MAC 2233 or higher).
Attributes: Artificial Intelligence
ECO 4422 Econometrics 2 4 Credits
Grading Scheme: Letter Grade
Introduces advanced concepts and methods employed in empirical economic analysis. Focuses on identification of causality using regression techniques. Examines regression discontinuity and difference-in-differences identification strategies.
Prerequisite: ECO 4421 OR (STA 4210 AND ECO 3101) OR (STA 4210 AND ECP 3703).
Attributes: Artificial Intelligence
EDF 3452 Generative AI for Educational Research 3 Credits
Grading Scheme: Letter Grade
Provides research experience in the field of generative AI in education through the selection and utilization of AI tools and technics appropriate for the development of an intelligent tutoring system for improving the oral reading fluency of elementary school students.
Prerequisite: COP3502C or COP3504C.
Attributes: Use-AI
EDF 4470 Survey Research Methods in Education 3 Credits
Grading Scheme: Letter Grade
Overview of the theory and application of survey research methods, with special emphasis on conducting survey research in educational settings. Presents the full process of survey research, including design, implementation, analysis, and data management.
Prerequisite: Sophomore standing or higher.
Attributes: Use-AI
EDP 3211 Cognitive and Educational Science in AI 3 Credits
Grading Scheme: Letter Grade
Examines cognitive and education science concepts that underpin the field of Artificial Intelligence and explores how AI integrates into educational contexts. Enhance conceptual understanding of what makes AI tools work and increase the ability to use those tools in an increasingly AI-rich educational environment. This non-programming course does not require advanced AI or computer science knowledge.
Prerequisite: (EDF 3210 or EDF 3110 or EDF 4140 or PSY 2012 or EXP 3604) and fundamental knowledge of cognition or education theories.
Attributes: Artificial Intelligence, Know-AI
EEE 3773 Introduction to Machine Learning 4 Credits
Grading Scheme: Letter Grade
Covers introductory topics in pattern recognition and machine learning and use of these methods towards a variety of real world applications. The focus of this course is to be introduced to basic machine learning concepts and how to use associated state-of-the-art machine learning tools.
Prerequisite: EEL 3135.
Attributes: Artificial Intelligence
EEE 4773 Fundamentals of Machine Learning 3 Credits
Grading Scheme: Letter Grade
Overview of machine intelligence and the role of machine learning in a variety of real-world problems. Probability and statistics to handle uncertain data. Topics covered include: learning models from data in both a supervised and unsupervised fashion, linear models and non- linear models for classification, and linear dimensionality reduction.
Prerequisite: EEL 3135 and EEL 3850 with minimum grades of C.
Attributes: Artificial Intelligence
EEL 3850 Data Science for ECE 4 Credits
Grading Scheme: Letter Grade
Analysis, processing, simulation, and reasoning of data. Includes data conditioning and plotting, linear algebra, statistical methods, probability, simulation, and experimental design.
Prerequisite: MAC 2312 and (COP2271 or COP2273 or COP2274 or COP3502 or COP3502C or COP3503 or COP3503C or COP3504 or COP3504C or EEL3834 or equivalent)
Attributes: Artificial Intelligence
EEL 3872 Artificial Intelligence Fundamentals 3 Credits
Grading Scheme: Letter Grade
An overview of Artificial Intelligence (AI), approaching the concept from its origins to expectations for the future. The course will focus on various AI technologies, how to build Machine Learning models, and how to apply AI tools to solve real-world problems. Some concepts that will be introduced in the course are types of AI and Machine Learning, Hacking and the IoT, AI today, and its outlook for the future.
Prerequisite: Junior standing or above, or instructor permission.
Attributes: Artificial Intelligence
EEL 4516 Noise in Devices and Communication Systems 3 Credits
Grading Scheme: Letter Grade
Origin, characterization and measurement of random noise. Calculation of signal-to-noise ratios and probability of errors in communication systems.
Corequisite: EEL 4514C.
Attributes: Artificial Intelligence
EEL 4665C Intelligent Machines Design Laboratory 4 Credits
Grading Scheme: Letter Grade
Design simulation, fabrication, assembly and testing of intelligent robotic machines. Laboratory.
Prerequisite: (EEL 3744C or EML 3005) or instructor permission.
Attributes: Artificial Intelligence
EEL 4837 Programming for Electrical Engineering 2 3 Credits
Grading Scheme: Letter Grade
Fundamentals of data structures and algorithms, including lists, queues, stacks, divide-and-conquer, dynamic programming, trees, tables, graphs and recursive techniques. The role of specific data structures in electrical engineering applications.
Prerequisite: EEL 3834 or COP 2274 or COP 3503C or COP 3504C or equivalent, all with minimum grades of C.
Attributes: Artificial Intelligence
EGM 3344 Introduction to Numerical Methods of Engineering Analysis 3 Credits
Grading Scheme: Letter Grade
Methods for numerical solution of mathematical problems with emphasis on engineering applications using MATLAB. Includes roots, optimization, linear algebraic equations, matrices, curve fitting, differentiation, integration and ordinary differential equations.
Prerequisite: MAC 2313 and COP 2271;
Corequisite: MAP 2302.
Attributes: Artificial Intelligence
EMA 3800 Error Analyses and Optimization Methodologies in Materials Research 3 Credits
Grading Scheme: Letter Grade
Statistical approach for materials research; basic and relevant statistical concepts; error analyses; factorial matrices; reducing the variance; nested designs and sampling plans; mixture designs; optimization technology; response surface method; and Taguchi.
Corequisite: EMA 3010 and (COP 2271 or COP 2273).
Attributes: Enable-AI
EME 2020 Making Sense: Understanding the World With Data and AI 3 Credits
Grading Scheme: Letter Grade
This course focuses on the bidirectional relationship of artificial intelligence (ai) and theories of learning. Data, combined with methods of ai, can help us make sense of the learning behaviors that emerge through data generated as teachers and students interact with educational technologies. Similarly, many advancements in ai have resulted from theories, models, and biology related to how humans learn. The usage of digital learning platforms, learner management systems, and other technologies is growing across educational settings. Combining ai with the quality data that has been produced by using these tools can improve both teacher instruction as well as learner experiences. This data can help understand the processes of learning beyond other assessment measures like correctness and be used to inform the development of better technologies and instructional content. Like any system designed for human interaction, the data collected by these systems can be messy, incomplete, and gen.
Attributes: Quest 2, Artificial Intelligence, General Education - Social Science
EML 4842 Autonomous Vehicles 3 Credits
Grading Scheme: Letter Grade
Methods and apparatus to automate vehicle navigation. Integration of sensors such as global positioning system (GPS), light detection and ranging (LIDAR), position encoders, inertial measurement units (IMU). Vehicle propulsion and steering. Python programming language, Linux operating system, robot operating system (ROS, ROS2). Autonomous navigation, obstacle avoidance, path planning, dead reckoning, vehicle localization.
Prerequisite: COP 2271 or COP 2273 or COP 2274.
Attributes: Artificial Intelligence
ENC 1602 Rhetoric of AI 3 Credits
Grading Scheme: Letter Grade
For every, “Okay, Google” or “Hey, Siri” to help us with tasks, there is the potential for “Help me understand something” and “Help me through something.” Thus, while we have the ability to give AI commands, conversational AI has the power to influence, persuade, and help us grow, all of which have equally interesting and alarming possibilities. In this course, we will examine this while testing our assumptions. By relying on multidisciplinary knowledge from anthropology, art, linguistics, philosophy, professional writing, psychology, rhetoric, and technical writing -including selections from the Western canon- to develop interdisciplinary skills in creative thinking, critical thinking, collaboration, presentations, public speaking, and research, our work in AI will necessarily bridge the humanities with the technical. To that end, we will begin with the history and theories of rhetoric and AI. Then, we will survey the landscape of AI developers and developments.
Attributes: Quest 1, Artificial Intelligence, General Education - Humanities, Know-AI, Satisfies 2000 Words of Writing Requirement
ENC 3310 Advanced Exposition 3 Credits
Grading Scheme: Letter Grade
Advanced composition course in methods of exposition: definition, classification, comparison and contrast, analysis, illustration and identification. (WR)
Prerequisite: Junior standing or higher and two 1000/2000-level English courses.
Attributes: Artificial Intelligence, Satisfies 6000 Words of Writing Requirement
ENC 3601 Professional Writing in AI 3 Credits
Grading Scheme: Letter Grade
Explores professional writing situations in the AI sector. Applies rhetorical theory, employs research methods, and requires technical writing to articulate persuasive materials. Learn how to imagine creative solutions with and for AI-driven innovations. For students interested in majors and careers which involve AI.
Prerequisite: ENC 1101.
Attributes: Artificial Intelligence, Satisfies 6000 Words of Writing Requirement, Use-AI
ENY 4161 Insect Classification 3 Credits
Grading Scheme: Letter Grade
Classification of major families of adult insects with emphasis on their identification, habitat and niche. A properly curated collection is required.
Prerequisite: ENY 3005 and ENY 3005L.
Attributes: Artificial Intelligence
ESI 4356 Decision Support Systems for Industrial and Systems Engineers 4 Credits
Grading Scheme: Letter Grade
Applications of decision support systems in industrial and systems engineering; developing and implementing decision support systems arising in industrial and systems engineering using popular database management and spreadsheet software.
Prerequisite: COP2273 or COP2271 with a minimum grade of C.
Corequisite: ESI 3312 will minimum grades of C.
Attributes: Artificial Intelligence
ESI 4610 Introduction to Data Analytics 3 Credits
Grading Scheme: Letter Grade
Provides a basic understanding of the skills necessary for managing and analyzing data. The concepts covered include exploratory data analysis, data manipulation, data cleaning, data wrangling, and machine learning models. A basic understanding of data management with SQL is also provided. All the technical skills will be motivated by different examples involving data. Python is the programming language used.
Prerequisite: COP2273 or COP2271 and ESI 3215C with minimum grades of C.
Attributes: Artificial Intelligence
EXP 4174C Laboratory in Sensory Processes 4 Credits
Grading Scheme: Letter Grade
Collect, analyze, and evaluate data on specific problems related to sensory and perceptual abilities.
Prerequisite: (EXP 3104 or EXP 3604) and STA 2023.
Corequisite: STA 3024.
Attributes: Artificial Intelligence
FAS 4175 Algae Biology and Ecology 3 Credits
Grading Scheme: Letter Grade
The biology and ecology of aquatic algae, including evolution, classification, structure, photosynthesis, growth, and reproduction. Emphasis on the ecological role of algae in different aquatic ecosystems (e.g. open ocean, estuaries, coral reefs, rocky intertidal), their impacts (e.g. harmful algae blooms, food webs), and their applications (e.g. food, biochemical).
Prerequisite: BSC 2010 and BSC 2010L, or equivalent.
Attributes: Artificial Intelligence
FES 3780 Analytical Approaches to Fire Protection 3 Credits
Grading Scheme: Letter Grade
Examines the tools and techniques of rational decision making in fire and emergency services agencies, including data collection, statistics, probability, decision analysis, utility modeling, resource allocation, and cost benefit analysis.
Prerequisite: junior or senior standing.
Attributes: Artificial Intelligence
FES 3815 Command and Control at Catastrophic Fire-Rescue Incidents 3 Credits
Grading Scheme: Letter Grade
Incident command at multiple-alarm incidents, emphasizing rapid fireground decision-making, safety, personnel accountability and communications. Settings for scenarios include multi-family occupancies, hotels, high-rises, healthcare facilities and large retail centers.
Prerequisite: junior standing.
Attributes: Artificial Intelligence
FIN 4128 Financial Plan Development 4 Credits
Grading Scheme: Letter Grade
Capstone course in financial planning. Covers retirement needs, individual, corporate, and government retirement plans, plus group benefits plans. Examines professional issues in financial planning, including ethical considerations, regulation and certification requirements, written and oral communication skills, and professional responsibility. Students develop a comprehensive financial plan.
Prerequisite: Any two of FIN 3124, FIN 4132, or RMI 3011.
Corequisite: FIN 3124 or FIN 4132 or RMI 3011 if not used above.
Attributes: Artificial Intelligence
FNR 3073 Florida’s Forest Communities 2 Credits
Grading Scheme: Letter Grade
Learn to recognize Florida forest communities and the dominant trees and common plants that grow in them. Using the principles of plant taxonomy and tree identification skills, identify common Florida forest trees by using visual physical plant characteristics coupled with habitat cues and tree species groupings. Finally, learn to apply these classifications to describe the conditions that underlie forest community distributions in Florida.
Prerequisite: Junior or senior standing.
Attributes: Artificial Intelligence
FOS 4064 Principles of Food Entrepreneurship 3 Credits
Grading Scheme: Letter Grade
Overview of the food business. Covers the fundamental aspects of food entrepreneurship, including basic food science, food regulation, food safety, product development, business planning, and marketing. Learn how various disciplines of food science are incorporated into entrepreneurship through lectures, hands-on activities, and presentations.
Prerequisite: Junior or Senior Standing.
Attributes: Enable-AI
FOS 4427C Principles of Food Processing 4 Credits
Grading Scheme: Letter Grade
Principles of processing foods: cooling, freezing, heating, dehydrating, concentrating, irradiating, fermenting and the use of chemicals.
Prerequisite: FOS 4410C.
Attributes: Artificial Intelligence
FOS 4731 Government Regulations and the Food Industry 2 Credits
Grading Scheme: Letter Grade
Government laws regulating food wholesomeness; food handling, processing and distribution under sanitary conditions; food ingredients and labeling of food products.
Prerequisite: FOS 3042 or Food Science major or instructor permission.
Attributes: Enable-AI
FRE 4780 Introduction to French Phonetics and Phonology 3 Credits
Grading Scheme: Letter Grade
An introduction to French phonological processes, providing explanatory evidence for the production of speech sounds, for the classification of sounds, for their interrelationship with one another (gliding, nasalization, assimilation), for morphological and syllable structure, for specifically French phenomena such as liaison, elision, final consonant drop, schwa drop, and for the relationship of morphology to phonology, especially in the verb system.
Prerequisite: FRE 3320; LIN 3010 recommended.
Attributes: Artificial Intelligence
GEO 2351 Geographical Sciences and Sustainability 3 Credits
Grading Scheme: Letter Grade
Examines the most critical environmental issues facing the world today; emphasizes the sustainability of both human and physical systems in the 21st century utilizing cutting-edge geographic technologies: spatial analysis, GIS, and satellite imagery.
Prerequisite: any Biological Sciences or Physical Sciences General Education course.
Attributes: Artificial Intelligence
GEO 4167C Intermediate Quantitative Analysis for Geographers 3 Credits
Grading Scheme: Letter Grade
Surveys various multivariate techniques commonly used to analyze geographic data. Emphasis on hypothesis testing, inference, multiple regression, analysis of variance and cluster analysis. Introduces time-series regression and grouped estimation procedures, factor analysis, probit/logit modeling and trend-surface interpolation. (WR)
Prerequisite: GEO 3162C or the equivalent.
Attributes: Artificial Intelligence, Satisfies 6000 Words of Writing Requirement
GEO 4169 Spatial Econometrics and Modeling 3 Credits
Grading Scheme: Letter Grade
Introduces regression models capable of dealing with spatial auto-correlation; develop statistical models and estimate with computer software.
Prerequisite: GEO 4167C or equivalent.
Attributes: Artificial Intelligence
GEO 4285 Water, Risk, and Extreme Events 3 Credits
Grading Scheme: Letter Grade
Investigates techniques for evaluating the risks of extreme events related to water in our environment. Presents data and methodologies for estimating the rarity of phenomena including excessive rainfall totals, high and low river levels, coastal storm surge and waves, and drought.
Prerequisite: GEO 3162C or STA 3032 or permission of instructor.
Attributes: Artificial Intelligence, Satisfies 6000 Words of Writing Requirement
GEO 4306C Geography of Vector-borne Diseases 3 Credits
Grading Scheme: Letter Grade
Introduces the spatial epidemiology of vector-borne diseases (VBDs) and geospatial methods for monitoring, mapping and modeling them. Provides hands-on experiences for mapping and modeling risk of VBDs via GIS-based labs.
Prerequisite: GEO 3452 or GIS 3043 or permission of the instructor.
Attributes: Artificial Intelligence
GER 1130 Beginning Intensive German 1 5 Credits
Grading Scheme: Letter Grade
The first semester of the basic language sequence for beginning learners of German. Emphasis is on communicating in German through speaking, listening, reading, writing, and culturally related topics. Communication in German is enhanced by the use of various authentic texts, music, film, and other materials.
Attributes: Enable-AI
GER 1131 Beginning Intensive German 2 5 Credits
Grading Scheme: Letter Grade
Second semester of the basic language sequence. Emphasizes communicating in German through speaking, listening, reading, writing and culturally related topics. Communication in German is enhanced by the use of various authentic texts, music, film, and other materials.
Prerequisite: GER 1130 with minimum grade of C, or S, or the equivalent.
Attributes: Enable-AI
GIS 2002 The Digital Earth 3 Credits
Grading Scheme: Letter Grade
Focuses on how the Earth's surface is visualized, explored, and analyzed in digital formats (e.g. maps, satellite images, aerial photos). Provides an introduction to fundamental concepts of digital geographic data to understand the Earth environment and human society based on the vast quantities of geographic information in our ever-changing world.
Attributes: Artificial Intelligence
GIS 3043 Foundations of Geographic Information Systems 4 Credits
Grading Scheme: Letter Grade
Geographic Information Systems (GIS) as the technology for creation, modification, display, and analysis of spatial information. Develops knowledge of GIS, competence in geographic databases, and familiarity with computer software and hardware.
Prerequisite: Sophomore standing or higher.
Attributes: Artificial Intelligence
GIS 3420C GIS Models for Public Health 3 Credits
Grading Scheme: Letter Grade
Focuses on the design of GIS-based models to address health and healthcare issues. Topics include a conceptual framework, landscape epidemiology models, disease diffusion models, health accessibility, human health behavior and location-allocation of health services. Laboratory section provides hands-on experience applying these models with GIS tools.
Prerequisite: (GIS 3043 or equivalent) & (STA 2023 or GEO 3162C or equivalent) or (Instructor permission)
Attributes: Artificial Intelligence
GIS 4037 Digital Image Processing 4 Credits
Grading Scheme: Letter Grade
Introduces the theory and application of digital imagery data in geographical research with a hands-on and lab-based approach.
Prerequisite: Junior standing or higher.
Attributes: Artificial Intelligence, Enable-AI
GIS 4102C GIS Programming 3 Credits
Grading Scheme: Letter Grade
Introduces basic programming concepts; instruction in popular programming languages for geospatial processing, applications, and modeling in ArcGIS environment.
Prerequisite: GIS 3043C or equivalent.
Attributes: Artificial Intelligence
GIS 4113 Introduction to Spatial Networks 3 Credits
Grading Scheme: Letter Grade
Many phenomena of interest in physical, social and cyber environments can be thought of as networks within geographic context. Teaches methods for analyzing these spatial networks, and introduces their applications in geography, transportation, hydrology, epidemiology, social science, etc.
Prerequisite: Entry level knowledge of statistics or instructor permission. Prior experience with ArcGIS is preferred.
Attributes: Artificial Intelligence
GIS 4115C Spatial Surface Modeling and Geostatistics 3 Credits
Grading Scheme: Letter Grade
Teaches principles for modeling and analyzing surfaces of geographic features, such as terrain, temperature, and diseases, with an emphasis on geostatistical (or kriging) analysis. Provides hands-on experiences of using ArcGIS Geostatistical Analyst through lab exercises.
Prerequisite: (STA 2023 or GEO 3162C or equivalent) and GIS 3043 or equivalent or instructor permission.
Attributes: Artificial Intelligence
GIS 4123C GeoAI – Geographic Artificial Intelligence 3 Credits
Grading Scheme: Letter Grade
Integration of Geography and AI, or GeoAI—a subfield of spatial data science—provides novel approaches for addressing a variety of geospatial problems in the natural environment and our human society. Hands-on computing labs use real-world geospatial data to address AI topics such as image classification, object detection, scene segmentation, simulation and interpolation, retrieval and question answering, on-the-fly data integration, and geo-enrichment.
Prerequisite: Any 3000 level or higher GIS prefix course [GIS3XXX, GIS4XXX] or permission of instructor.
Attributes: Artificial Intelligence, Build-AI
GIS 4324 GIS Analysis of Hazard Vulnerability 3 Credits
Grading Scheme: Letter Grade
Geographic and cartographic techniques for geospatial analysis of risk, vulnerability, and resilience using ArcGIS. Learn to utilize physical and human geographic datasets for multiple hazard contexts including hydrometeorological, climatological, and geophysical hazards.
Prerequisite: GIS 3043 or URP 4273 with minimum grade of C.
Attributes: Artificial Intelligence
GIS 4500 Population GIS 3 Credits
Grading Scheme: Letter Grade
Instruction on geographic and cartographic techniques for geospatial analysis of population, demographic, and socioeconomic data using ArcGIS Pro. Students identify and utilize current and historical secondary population data sources for GIS analysis of population changes, and for mapping of segregation, inequality, and well-being indicators.
Prerequisite: GIS 3043 or GIS 3001C or URP 4273 with minimum grades of C, or GEO 3430 or SYD 4020.
Attributes: Artificial Intelligence
HFT 4442 Artificial Intelligence Revolutions and Applications in Tourism, Hospitality, and Events 3 Credits
Grading Scheme: Letter Grade
Foundational examination of the implications of the artificial intelligence revolution in the tourism, hospitality, and event industry. Includes analyses of AI applications in booking, transportation, theme parks, destination and attraction marketing, economic, social, cultural, and environmental impacts, as well as motivators to travel.
Prerequisite: Junior or Senior Standing.
Attributes: Artificial Intelligence
HFT 4446C GIS and Spatial Analysis for Tourism and Social Data 3 Credits
Grading Scheme: Letter Grade
Utilizes the opportunities provided by dynamically developing methods of geographical information systems (GIS) for visualization and geographic analysis of the data. Students will learn basic skills in working with the industry-standard ESRI ArcGIS software and apply their newly acquired knowledge in solving model problems in tourism research, planning, and development.
Prerequisite: Junior standing or higher
Attributes: Artificial Intelligence
HFT 4746 Smart Cities, Attractions, and Theme Parks 3 Credits
Grading Scheme: Letter Grade
Provides the foundation needed to design smart tourism places. Examines relationships between technology, traveler behavior, and the travel industry. Learn to integrate technology, analytics, marketing, and the design of tourism cities, attractions, and theme parks. Focuses on sustainable/safe/healthy environments with cutting-edge technologies including Artificial Intelligence (AI) and Data Science.
Prerequisite: Junior or Senior Standing.
Attributes: Artificial Intelligence
HOS 4283C Advanced Organic and Sustainable Crop Production 3 Credits
Grading Scheme: Letter Grade
Intensive examination of the methods and techniques necessary for organic and sustainable production and marketing of horticultural products.
Prerequisite: HOS 3281C.
Attributes: Artificial Intelligence
HSA 4191 Health Informatics & Emerging Healthcare Technologies 3 Credits
Grading Scheme: Letter Grade
Provides a fundamental understanding health informatics, healthcare information systems, and emerging healthcare technologies, starting with the core informatics competencies and the foundation of knowledge model.
Prerequisite: PHC 4101 or HSA 3111 or instructor permission.
Attributes: Artificial Intelligence
HSC 4063 AI Health Promotion and Vulnerable Populations 3 Credits
Grading Scheme: Letter Grade
Exploration of how AI technologies can be ethically and effectively used for health promotion, disease prevention, and health advocacy. Examines cross-cultural concepts of health and illness and social determinants of health affecting vulnerable populations in the US.
Prerequisite: HEB_BS & Junior or Senior standing.
Attributes: Know-AI
HSC 4557 Medical Pharmacology in Clinical Care 3 Credits
Grading Scheme: Letter Grade
Overview of the fundamental principles of pharmacology, including drug classifications, mechanisms of action, and their impact on the human body. Explore the history of pharmacology, drug development, and the ethical considerations surrounding drug use and regulation.
Prerequisite: OTH 3416 with a minimum grade of C and (HES_BHS major or HPR_UMN minor).
Attributes: Artificial Intelligence
HUN 4446 Nutrition and Disease: Part 2 3 Credits
Grading Scheme: Letter Grade
Part two of the sequence that focuses on the biochemical and pathophysiological bases of disease/conditions that require specialized nutrition support/medical nutrition therapy.
Prerequisite: HUN 4445 and (BCH 3025 or BCH 4024) and (PCB 4723C or APK 2105C).
Corequisite: DIE 4246.
Attributes: Artificial Intelligence
HUN 4813C Laboratory Techniques in Molecular Nutrition 3 Credits
Grading Scheme: Letter Grade
Laboratory techniques relevant to the study of nutrition, ranging from biochemistry, molecular biology, genomics and bioinformatics.
Prerequisite: CHM 2211 and CHM 2211L;
Corequisite: BCH 3025 or BCH 4024.
Attributes: Artificial Intelligence
IDS 2935 Special Topics 1-3 Credits
Grading Scheme: Letter Grade
Introduces selected interdisciplinary topics. Content varies from term to term.
Attributes: Quest 1, Know-AI
ISM 3004 Computing in the Business Environment 4 Credits
Grading Scheme: Letter Grade
Presents fundamental concepts from two perspectives: the individual business computer user and the corporate business computing environment. Introduces common business computing applications; this is not a hands on applications training course. Students use their existing computer skills to complete assignments.
Prerequisite: basic skills for Microsoft Word, PowerPoint, and Excel.
Attributes: Artificial Intelligence
ITA 2220 Intermediate Italian 1 4 Credits
Grading Scheme: Letter Grade
Enhances knowledge of Italian in all four skills: listening, reading, speaking, and writing. The goal is to create communicative competence that enables advancement to third year study and to benefit from Italy visits.
Prerequisite: ITA 1131 or the equivalent.
Attributes: Enable-AI, General Education - International
JOU 3363 Introduction to Web Apps for Communicators 3 Credits
Grading Scheme: Letter Grade
Introduces web markup, coding, and programming for journalism and communications students with no prior coding experience. Explore media-industry best practices for front-end web development, problem solving and algorithmic thinking, and recent examples of interactives and apps from media organizations.
Prerequisite: junior standing in college or instructor permission.
Attributes: Artificial Intelligence
JOU 3365 Artificial Intelligence in Media and Society 3 Credits
Grading Scheme: Letter Grade
Gain an understanding of artificial intelligence as it applies to the media professions, including journalists reporting on AI. Explore developments in AI technologies as covered by the mass media. Learn to detect exaggeration in descriptions of AI’s promise and potential risks and dangers.
Prerequisite: Junior standing or higher.
Attributes: Artificial Intelligence
LAA 1330 Site Analysis 3 Credits
Grading Scheme: Letter Grade
Inventory, analysis and evaluation of site development procedures; emphasis on landscape ecology.
Attributes: Artificial Intelligence
LAA 3394C Advanced Design Communication 3 Credits
Grading Scheme: Letter Grade
Focuses on advanced-level digital tools and techniques used in landscape architecture.
Prerequisite: LAA 2376C and LAA 2379C.
Attributes: Artificial Intelligence
LAE 4604 Language Arts for Diverse Learners in Early Education 3 Credits
Grading Scheme: Letter Grade
Focuses on the early developmental levels of writing and different definitions of writing, including writing as composing, writing as spelling/encoding and writing as handwriting. Addresses instructional strategies appropriate for teaching young children to write and explores instructional approaches from different theoretical perspectives.
Prerequisite: RED 3309.
Attributes: Artificial Intelligence
LIN 4005 Stats for Linguists 3 Credits
Grading Scheme: Letter Grade
Introduces the concepts of probability and statistics, with examples chosen mainly from linguistics. Topics include descriptive statistics, comparing means, regression, t-tests, linear mixed models, and basic experimental design.
Prerequisite: LIN 2011.
Attributes: Artificial Intelligence, Enable-AI
LIN 4770C Introduction to Computational Linguistics 3 Credits
Grading Scheme: Letter Grade
Introduces the study of natural language from a computational perspective. Discusses both applied (engineering) and theoretical (cognitive) issues, ranging from speech and language technology to formal aspects of theoretical linguistic models. Covers a brief introduction to programming in Python, and the basics of Natural Language Processing and their applications.
Prerequisite: LIN 2011.
Attributes: Artificial Intelligence
MAD 2502 Intro to Computational Math 3 Credits
Grading Scheme: Letter Grade
Introduces mathematical computation and the Python programming language. Emphasizes using mathematical algorithms to solve problems in analysis, number theory, combinatorics, algebra, linear algebra, numerical analysis, and probability.
Prerequisite: MAC 2311 or MAC 3472, minimum grade of C.
Attributes: Artificial Intelligence, Enable-AI
MAD 3107 Discrete Mathematics 3 Credits
Grading Scheme: Letter Grade
Logic, sets, functions; algorithms and complexity; integers and algorithms; mathematical reasoning and induction; counting principles; permutations and combinations; discrete probability. Advanced counting techniques and inclusion-exclusion.
Prerequisite: MAC 2312 or MAC 2512 or MAC 3473 with a minimum grade of C.
Attributes: Artificial Intelligence
MAN 4504 Operations and Supply Chain Management 4 Credits
Grading Scheme: Letter Grade
Managerial concepts and quantitative tools required in the design, operation, and control of production systems and their relationship to business functions.
Prerequisite: BUL 4310 and FIN 3403 and GEB 3373 and MAC 2233 or MAC 2311 and MAN 3025 and MAR 3023 and QMB 3250 and STA 2023 and (Business major or Accounting major)
Attributes: Artificial Intelligence
MAP 4102 Probability Theory and Stochastic Processes 2 3 Credits
Grading Scheme: Letter Grade
Random walks and Poisson processes, martingales, Markov chains, Brownian motion, stochastic integrals and Ito's formula.
Prerequisite: STA 4321 with a minimum grade of C.
Attributes: Artificial Intelligence
MAS 3114 Computational Linear Algebra 3 Credits
Grading Scheme: Letter Grade
Linear equations, matrices and determinants. Vector spaces and linear transformations. Inner products and eigenvalues. Emphasizes computational aspects of linear algebra.
Prerequisite: MAC 2312, MAC 2512 or MAC 3473 with a minimum grade of C and experience with a scientific programming language.
Attributes: Artificial Intelligence
MAS 4115 Linear Algebra for Data Science 3 Credits
Grading Scheme: Letter Grade
A second course in linear algebra, focusing on topics that are the most essential for data science. Introduces theory and numerical methods required for large data-sets and machine learning. Topics include LU, QR, and singular-value decompositions; conditioning and stability; the DFT and filters; deep learning; fully connected and convolutional nets.
Prerequisite: (MAS 3114 or MAS 4105) and MAC 2313.
Attributes: Build-AI
MET 4224C Machine Learning in Meteorology 3 Credits
Grading Scheme: Letter Grade
Hands-on experiences with Machine Learning (ML) from a series of practical case-studies in meteorology. Regression, classification, clustering and retrieval, and deep learning to solve research questions by identifying potential applications of ML, selecting appropriate ML models, representing data as features to serve as input to ML models, and assessing model quality
Prerequisite: Any 3000 level or higher MET prefix course or permission of instructor.
Attributes: Artificial Intelligence
MET 4410 Radar and Satellite Meteorology 3 Credits
Grading Scheme: Letter Grade
Overview of radar and satellite remote sensing as used in the atmospheric sciences, including the principles of atmospheric radiative transfer, the retrieval of atmospheric variables, and emphasis on geospatial interpretation of imagery for different weather systems.
Prerequisite: PHY 2049 and MET 3503.
Attributes: Artificial Intelligence
MET 4560 Atmospheric Teleconnections 3 Credits
Grading Scheme: Letter Grade
Atmospheric teleconnections are recurring large-scale patterns of pressure and circulation anomalies. They can influence temperature, rainfall, storm tracks and jet stream location and intensity. Examines how these patterns were discovered, how the index that characterizes the phase of each teleconnection is calculated and the weather associated with different phases.
Prerequisite: MET 3503 or GEO 3250 with a minimum B- grade.
Attributes: Artificial Intelligence
MET 4750 Spatial Analysis of Atmospheric Data using GIS 3 Credits
Grading Scheme: Letter Grade
How atmospheric data are collected and analyzed for meteorologic and climatologic-scale research. Where various types of data are obtained and how to analyze data to answer specific research questions.
Prerequisite: GEO 3250 or MET 3503 or MET 4532
Attributes: Artificial Intelligence
MGF 1106 Mathematics for Liberal Arts Majors 1 3 Credits
Grading Scheme: Letter Grade
For non-science and non-business majors. Includes an introduction to set theory, logic, number theory, probability, statistics, graphing, and linear programming.
Attributes: Artificial Intelligence
MMC 3420 Consumer and Audience Analytics 3 Credits
Grading Scheme: Letter Grade
Provides practical analytical skill-sets, benefiting those who plan careers in analytics/research, social media, media business, advertising/marketing, and public relations.
Prerequisite: Junior standing or higher.
Attributes: Artificial Intelligence
MUN 2022L Laptop and Electronic Arts Ensemble 1 Credit
Grading Scheme: Letter Grade
Explores composing and coding for and performance with live electronic musical instruments. Also covers fundamentals of sound synthesis, audio programming, instrument design, human-computer interaction, sound-driven multimedia, data sonification, and electronic concert production and documentation. Using the instruments that they design, students perform their electronic works and works of their colleagues during a two concert experiences over the course of the semester.
Attributes: Artificial Intelligence
MUT 3622 Musical Data Structures 3 Credits
Grading Scheme: Letter Grade
Engage with computational methods for the analysis and generation of musical materials and structures to better understand how humans produce and interact with them. Topics covered include music representation and encoding, music information retrieval, corpus studies, machine improvisation, and procedural music generation. These techniques will be considered in the context of many different aesthetics, styles, genres, theoretical frameworks, and societal contexts.
Prerequisite: Junior standing or higher.
Attributes: Artificial Intelligence
NUR 4827 Lead and Inspire 4: Leadership and Innovation in Nursing Practice 2 Credits
Grading Scheme: Letter Grade
Synthesize the roles, functions, and perspectives of the professional nurse utilizing the lead and inspire concepts; emphasizes leadership and innovation to transform professional nursing practice and healthcare systems.
Prerequisite: NUR 4108 and NUR 4467C and NUR 4768C.
Attributes: Artificial Intelligence
PHC 3621 Ethics in Artificial Intelligence: Who’s Protecting Our Health 3 Credits
Grading Scheme: Letter Grade
Explores the ethical challenges of using artificial intelligence in healthcare and the practice of public health. Examine predictive models used for making important health decisions, addressing factors that contribute to trustworthy artificial intelligence in health, analyzing potential for bias, risk, and social inequity in assessing and delivering health and public health interventions.
Prerequisite: PHC 3793.
Attributes: Artificial Intelligence, Ethical-AI
PHC 3793 Higher Thinking for Healthy Humans: AI in Healthcare and Public Health 3 Credits
Grading Scheme: Letter Grade
Covers history, foundational concepts, and methods on Artificial Intelligence (AI), focusing on public health and healthcare applications, including hands-on practice on graphical/high-level AI software. Doesn't include advanced statistical/machine learning training or programming.
Prerequisite: STA 2023 or equivalent.
Attributes: Artificial Intelligence
PHC 4792 Data Visualization in the Health Sciences 3 Credits
Grading Scheme: Letter Grade
Learn the foundations of information visualization and sharpen skills in understanding, evaluating, and presenting AI-driven public health data. R is primarily used to explore concepts in graphic design, storytelling, data wrangling and plotting, biostatistics, and artificial intelligence.
Prerequisite: STA 2023 or equivalent.
Attributes: Artificial Intelligence
PHC 4796C Artificial Intelligence in Psychological and Brain Sciences 3 Credits
Grading Scheme: Letter Grade
Builds upon the artificial intelligence (AI) foundations taught in PHC 3793 to train health science-focused students to examine how AI and Machine Learning (ML) methods are applied in psychology and related brain sciences, as well as to address the factors that contribute to appropriate use of AI. The course neither provides nor necessitates prior programming knowledge, advanced statistical, or machine learning training.
Prerequisite: (PHC 3793 or equivalent AI Foundations course with a grade of C) or [(HES_BHS major or ABS_UMN minor) and CLP 4420 with a minimum grade of C)].
Attributes: Artificial Intelligence
PHI 1680 AI, Philosophy, and Society 3 Credits
Grading Scheme: Letter Grade
In the past few years, the capabilities of AI-based systems have grown explosively due to the development of a new technology, large language models. These systems, known informally as “chatbots,” are trained on a significant portion of the text and images that humanity has collectively produced over centuries. As a result, they have developed the ability to perform tasks that we normally associate with human-level intelligence, such as writing essays, writing computer programs, and passing graduate-level exams. In this course, we will explore the philosophical and social implications of this powerful new technology. This course will engage with research from several academic disciplines, including computer science, psychology, philosophy, and economics, as well as the Western canon.
Attributes: Quest 1, Artificial Intelligence, General Education - Humanities
PHI 2631 Ethics and Innovation 3 Credits
Grading Scheme: Letter Grade
Grounding in ethical theory and moral reasoning with a focus on changes at both organizational and societal levels, including, for instance, technological innovations, new business practices, and legal changes. Examines the rights and responsibilities of those making such changes as well as the conditions that facilitate responsible decision making.
Attributes: Artificial Intelligence, General Education - Humanities, Satisfies 4000 Words of Writing Requirement
PHI 3681 Ethics, Data, and Technology 3 Credits
Grading Scheme: Letter Grade
Addresses ethical issues related to data science, algorithmic decision-making, and artificial intelligence. Pairs theoretical discussions of ethics, economics, and policy-making with concrete issues in emerging technologies.
Prerequisite: PPY_BA or PPY_UMN or DAT_BS or PHH####or PHI#### or PHM#### or PHP#### or IDS1114 with a minimum grade of C or sophomore standing or higher.
Attributes: Artificial Intelligence
PHI 3693 Ethics of Communication 3 Credits
Grading Scheme: Letter Grade
Examines ethical issues in communication between individuals and in the media. Topics include truth-telling, misrepresentation, privacy, and fairness.
Prerequisite: PPY_BA or PPY_UMN or PHH#### or PHI#### or PHM#### or PHP#### or IDS1114 with a minimum grade of C or sophomore standing or higher.
Attributes: Artificial Intelligence
PHY 2005 Applied Physics 2 3 Credits
Grading Scheme: Letter Grade
Continuation of the sequence. Electric and magnetic fields. Geometrical, wave and applied optics. Modern and nuclear physics. This course affords students the ability to critically examine and evaluate the principles of the scientific method, model construction, and use the scientific method to explain natural experiences and phenomena.
Prerequisite: PHY 2004 or equivalent.
Attributes: Enable-AI, General Education - Physical Science
PHZ 3152 Advanced Computational Techniques 3 Credits
Grading Scheme: Letter Grade
Advanced Computational Techniques in Astronomy and Physics. Advanced techniques in computational methods in the natural sciences and numerical analysis. Includes version controlling and programming in distributed environments; grid construction and convergence techniques; numerical differentiation; linear algebra; root finding; differential equations; Monte Carlo simulations; open source project development.
Prerequisite: MAC 2312 or equivalent.
Attributes: Artificial Intelligence
PLS 3223 Plant Propagation 2 Credits
Grading Scheme: Letter Grade
Principles, practices and physiological aspects of the propagation of horticultural and agronomic crops by cuttage, graftage, seedage, micropropagation and other methods.
Prerequisite: BOT 2010C or BSC 2010;
Corequisite: PLS 3223L.
Attributes: Artificial Intelligence
PLS 3223L Plant Propagation Laboratory 1 Credit
Grading Scheme: Letter Grade
Methods of propagating by seeds, bulbs, divisions, layering, cuttings, budding, grafting and micropropagation in a hands-on environment.
Prerequisite: BOT 2010C or BSC 2010.
Attributes: Artificial Intelligence
PSB 4342 Introduction to Cognitive Neuroscience 3 Credits
Grading Scheme: Letter Grade
The biological foundations of human cognition.
Prerequisite: PSB 3340 or instructor permission.
Attributes: Artificial Intelligence
PSB 4343C Laboratory in Cognitive Neuroscience 4 Credits
Grading Scheme: Letter Grade
Practical training in the foundations of cognitive neuroscience with a strong focus on cognitive experiments with human participants. Engage in theoretical work and practical experiments addressing behavioral, cognitive, and physiological processes relationships between biological processes.
Prerequisite: PSB 3340 and EXP 3604 and PSY 3213L and STA 2023.
Attributes: Artificial Intelligence
QMB 3250 Statistics for Business Decisions 4 Credits
Grading Scheme: Letter Grade
Correlation and linear regression, model building, multiple regression, analysis of variance, time series analysis and decision analysis. Regression modeling with computer applications for business problems.
Prerequisite: STA 2023. Open only to students who need this course for their major or who have permission from the WCBA.
Attributes: Artificial Intelligence
QMB 3302 Foundations of Business Analytics and Artificial Intelligence (AI) 4 Credits
Grading Scheme: Letter Grade
Introduces the basics of data analytics and machine learning using the powerful programming language Python. Learn Python basics, how to write programs, and how to use Python to solve real-world problems.
Prerequisite: MAC 2233 OR MAC 2311.
Attributes: Artificial Intelligence
QMB 4701 Managerial Operations Analysis 1 2 Credits
Grading Scheme: Letter Grade
Introduces the concepts and applications of management science; become more confident in understanding and using deterministic analytic models.
Prerequisite: MAC 2233 and STA 2023.
Attributes: Artificial Intelligence
QMB 4702 Managerial Operations Analysis 2 2 Credits
Grading Scheme: Letter Grade
Overview of stochastic applications of management science; learn stochastic modeling techniques and introductory visual basic.
Prerequisite: QMB 4701.
Attributes: Artificial Intelligence
RTV 3432 Ethics and Problems in Media 3 Credits
Grading Scheme: Letter Grade
Investigation and discussion of social problems, ethics, and responsibilities in media.
Prerequisite: RTV 2100 and RTV 3001 and RTV 2405 and junior standing or higher.
Attributes: Artificial Intelligence
RTV 3700 Media Law and Policy 3 Credits
Grading Scheme: Letter Grade
Introduction to the laws and regulations affecting the past, present, and future of communication technology, emphasizing free expression, privacy, defamation and intellectual property.
Prerequisite: (RTV 2100 or MMC 2100) and RTV 3001 with minimum grade of C.
Attributes: Artificial Intelligence
RTV 4420 New Media Systems 3 Credits
Grading Scheme: Letter Grade
Reviews technological development, applications, and implications in media systems; explores relationship between media, technological development and other societal forces to learn to evaluate the future of media systems.
Prerequisite: (RTV 2100 or MMC 2100) and RTV 3001 with minimum grade of C and junior standing or higher or instructor permission.
Attributes: Artificial Intelligence
RTV 4800 Media Management and Strategy 3 Credits
Grading Scheme: Letter Grade
Concepts and applications in media management and relevant strategic practices, including marketing, business intelligence, finance, management/leadership, strategic planning, innovations, and decision-making in the context of media related industries.
Prerequisite: RTV 4500 and (RTV 4506 or MMC 3420).
Attributes: Artificial Intelligence
RUS 1131 Introduction to Russian Language and Culture 2 5 Credits
Grading Scheme: Letter Grade
Continuation of introductory language and cultural study.
Prerequisite: RUS 1130 with minimum grade of C or S, or the equivalent.
Attributes: Enable-AI
SPM 3703 Sport Performance Analytics 3 Credits
Grading Scheme: Letter Grade
Introduces sport analytics with a strong emphasis on performance analytics. Through theoretical principles and hands-on practical experience utilizing the R programming language, students will acquire the skills and knowledge required to analyze sport performance data and leverage these insights to comprehend athletic performance. Learn how to make data-informed decisions that directly impact sport management strategies and overall performance in the sports world.
Prerequisite: STA 2023 & Sophomore standing & above.
Attributes: Artificial Intelligence
SPM 4725 Advanced Legal Aspects in Live Entertainment and Sports 3 Credits
Grading Scheme: Letter Grade
Concentrates on the legal aspects of the live entertainment and sports industry to provide a basic understanding of intellectual property, torts, and negligent acts. The goal is to avoid or reduce the probability of legal liabilities in the live entertainment and sports industry.
Prerequisite: (SPM 4723 or SPM 4724) & junior standing or higher.
Attributes: Artificial Intelligence
STA 3032 Engineering Statistics 3 Credits
Grading Scheme: Letter Grade
Basic concepts in probability, statistics, engineering applications. Use of computational methods, logic, and reasoning. Formulation of mathematical models, application of statistical techniques, and solution of real-world problems. Topics include probability, random variables, estimation, hypothesis testing, correlation, regression, and analysis of variance.
Prerequisite: MAC 2311.
Attributes: Artificial Intelligence
STA 3100 Programming With Data in R 3 Credits
Grading Scheme: Letter Grade
Introduction to statistical computing and programming with data. Topics include basic programming in R; data types and data structures in R; importing and cleaning data; specifying statistical models in R; statistical graphics; statistical simulation using pseudo-random numbers; reproducible research and the documentation of statistical analyses.
Prerequisite: STA 2023 with a minimum grade of B or (STA 3024 or STA 3032 with a minimum grade of B-) or (AP statistics score of 4 or 5).
Attributes: Artificial Intelligence
STA 3180 Statistical Modelling 3 Credits
Grading Scheme: Letter Grade
Overview of modern statistical modeling. Topics include linear regression, binary regression and classification, cross-validation, nonlinear regression and smoothing, tree-based methods, the bootstrap, and causal inference. Approaches will be illustrated in R.
Prerequisite: STA 3100.
Attributes: Artificial Intelligence
STA 4210 Regression Analysis 3 Credits
Grading Scheme: Letter Grade
Simple linear regression and multiple linear regression models. Inference about model parameters and predictions, diagnostic, and remedial measures about the model, independent variable selection, multicollinearity, autocorrelation, and nonlinear regression.
Prerequisite: STA 3100 and (STA 3024 or STA 3032 or STA 4321 or MAS 3114 or MAS 4105).
Attributes: Artificial Intelligence
STA 4241 Statistical Learning in R 3 Credits
Grading Scheme: Letter Grade
Overview of the field of statistical learning. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. Approaches will be illustrated in R.
Prerequisite: STA 4322 and STA 4210 and (MAS 3114 or 4105).
Attributes: Artificial Intelligence
STA 4321 Introduction to Probability 3 Credits
Grading Scheme: Letter Grade
Introduces the theory of probability, counting rules, conditional probability, independence, additive and multiplicative laws, Bayes Rule. Discrete and continuous random variables, their distributions, moments and moment generating functions. Multivariate probability distributions, independence, covariance. Distributions of functions of random variables, sampling distributions, central limit theorem.
Prerequisite: MAC 2313 or MAC 3474 with a minimum grade of C.
Attributes: Artificial Intelligence
STA 4502 Nonparametric Statistical Methods 3 Credits
Grading Scheme: Letter Grade
Introduction to nonparametric statistics, including one- and two-sample testing and estimation methods, one- and two-way layout models and correlation and regression models.
Prerequisite: STA 2023 or STA 3032 or STA 4210 or STA 4322.
Attributes: Artificial Intelligence
STA 4702 Multivariate Statistical Methods 3 Credits
Grading Scheme: Letter Grade
Review of matrix theory, univariate normal, t, chi-squared and F distributions and multivariate normal distribution. Inference about multivariate means including Hotelling's T2, multivariate analysis of variance, multivariate regression and multivariate repeated measures. Inference about covariance structure including principal components, factor analysis and canonical correlation. Multivariate classification techniques including discriminant and cluster analyses. Additional topics at the discretion of the instructor, time permitting.
Prerequisite: (STA 3024 or STA 4210 or STA 4322 or STA 6127 or STA 6167) and (MAS 3114 or MAS 4105 or the equivalent).
Attributes: Artificial Intelligence
SUR 4380 Remote Sensing 3 Credits
Grading Scheme: Letter Grade
Remote sensing systems, ground truthing, image classification systems, mapping applications, applications in plant and animal science, urban planning, engineering, geology, and integration into geographic information systems.
Prerequisite: Geomatics major of senior standing.
Attributes: Artificial Intelligence
SWS 4715C Environmental Pedology 4 Credits
Grading Scheme: Letter Grade
Study and analysis of soil in the environment and the factors responsible for soil formation and geographic distribution. Development of hydric soil criteria and hydric soil indicators. Emphasis on morphology or hydric/ non-hydric soils and introduction to diagnostic horizons and soil classification. Course also includes abs on soil field techniques.
Prerequisite: SWS 3022.
Attributes: Artificial Intelligence
URP 4283 Automation for Geospatial Modeling and Analysis 3 Credits
Grading Scheme: Letter Grade
Covers methods and techniques for automating geospatial modeling and analysis for planning and built environment by using visual models, computer programming, and custom-built applications and tools that utilize Geographic Information Systems (GIS) technology in the context of planning information systems.
Prerequisite: URP4000 & URP4273 & Junior standing or higher.
Attributes: Artificial Intelligence
WST 3610 Gender, Race and Science 3 Credits
Grading Scheme: Letter Grade
Feminist theories of nature, science, and technology, and how gender and race are critical to the origins of science, the making of scientists, and the politics of contemporary practice.
Prerequisite: (3 credits of WST) or (sophomore standing or higher).
Attributes: Artificial Intelligence, Enable-AI
WST 4002 Data Feminisms 3 Credits
Grading Scheme: Letter Grade
Draws from critical data and algorithm studies and feminist science and technology studies to develop critical tools of inquiry needed to approach data within a context of racialized, gendered, colonial, and classed systems of power. Combines practical data workshops with critical readings to analyze data across key uses in domains such as healthcare, security apparatuses, carceral systems, and digital infrastructures.
Prerequisite: Sophomore standing or higher.
Attributes: Ethical-AI