AI Courses at UF
University of Florida AI Course Form
Please complete this survey if you teach an AI focused course or a course with some AI aspect that you would like included in this list.
| Dept | Description | |
|---|---|---|
| Art and Art History | ART4645 Sensors | |
| Art and Art History | ART4612C Digital Media Workshop | |
| Art and Art History | ART4630C Video Art: Advanced Video Projects | |
| Art and Art History | ART6925C Art + Technology Workshop | |
| Finance, Insurance and Real Estate | FIN3403 Business Finance | Finance Modeling - No indicator of Excel skill requirements in syllabus |
| ISOM | ISM3004 Computing in the Business Environment | Understanding Software, PC at work, Excel Skills, Social Media, Data, Tableau, InfoSec, Digital |
| ISOM | ISM3013 Introduction to Information Systems | MS Office Skills - Certification in Excel and Access |
| Management | Man4504 Operations & Supply Chain Management | Forecasting, Planning, Queing Theory, Quality Control, Inventory Models |
| QMB3250 Statistics for Business Decisions | Statistics, Business Statistics | |
| QMB4701 Managerial Operations Analysis I | Deterministic Decision Modeling | |
| QMB4702 Managerial Operations Analysis II | Stochastic Decision Modeling, Queueing, Project Management | |
| QMB4930 Business Data Analytics | Business Data Analytics | |
| QMB3XXX Foundations of Business Analytics & Artificial Intelligence | This is a new course and it has been approved by UCC | |
| Agricultural&Biological Engineering | AOM4435 Precision Ag | AI introduction on history, current status, and example applications in crop production |
| Agricultural&Biological Engineering | ABE4662 Quantifications of Biological Processes | Student Project Using TensorFlow to identify species of trees through image analyses (a student selected project). Plan is to develop a module in AI to perform model selection comparisons with mechanistic vs. statistical (AI) models for biological processes and/or an AI image processing module to calculate key image traits (currently we have one for pod length). |
| Agricultural&Biological Engineering | ABE4171 Power and Machinery in Biological Engineering | Design and specification of power and machine elements applied to agricultural, biological and land and water resources or food engineering; fundamentals of power units, design of machine elements and power transmission. |
| Agronomy | PCB2441 Biological Invaders | includes discussion of detecting, mapping, and predicting invasive species using remote sensing, with a brief mention of AI. |
| Environmental Horticulture | PLS3223/L Plant Propagation | discuss the use of AI as a way to manage large-scale production systems, assess criteria like seed germination and seedling vigor, and as a control method for production environments in greenhouses and labs. The students learn about the equipment and processes, we discuss them in comparison to our own hands-on techniques, and we look at some articles and examples. Due to the nature of my course, AI hasn’t been the focus in these aspects of the content but rather it’s presented as a tool available to the commercial plant propagator and researcher. The students aren’t currently required to use, learn the details of, or develop the technology itself-if that’s the info your seeking then my apologies for the extra email. |
| Food Science & Human Nutrition | FOS4427C Principles of Food Processing | This course includes three lectures on modelling of microorganism growth and water isotherms using non-linear algorithms. These algorithms learn and find solutions based upon the data available. There is also discussion on how this has changed the field of food processing, and how it is likely to be used in the future as more data becomes available and there will be a greater need to optimize food processes. |
| Food Science & Human Nutrition | HUN4446 Nutrition and Disease 2 | Our first unit is nutritional genomics where students receive a broad overview of how food and one’s genotype can interact; i.e. how one’s genotype can impact how we “use” nutrients from digestion to metabolism, storage, etc, as well as how what we eat may influence gene expression. This is carried throughout the semester where there are known interactions such as with Celiac disease, diabetes, and hypertension to list a few. |
| Food Science & Human Nutrition | HUN4936 Nutritional Epigenomics and Metabolic Disease | this course discusses the study of the interactions between nutrients and genes in whole cell, organ, or organism, using high-throughput measurements and bioinformatics tools. |
| Food Science & Human Nutrition | HUN4813C Lab Techniques in Molecular Nutrition | this course covers omics methods, including genomics, proteomics, transcriptomics, and metabolomics, that use high-throughput measurements and bioinformatics tools to study genes, proteins, transcripts, and metabolites in whole cell, organ, or organism. |
| Horticultural Sciences | HOS4283C Advanced Organic and Sustainable Crop Production | |
| Horticultural Sciences | IDS2945 Fighting Food Waste and Loss | |
| Microbiology & Cell Science | ALS3200C AI in Agricultural and Life Sciences | UNIX+Python+Tensorflow - with applications in Ag/Life sci |
| Microbiology & Cell Science | BSC2891 Python Programming for Biologists | basic UNIX and Python foundations, data analysis in UNIX, bioinformatics |
| Microbiology & Cell Science | BSC4913/14 Bioinformatics Independent Research | student-driven independent computational research |
| Microbiology & Cell Science | MCB4325C R for Functional Genomics | statistics, functional genomics data analysis, R programming |
| Soil and Water Sciences | AGG4502 Nanotechnology in Food, Agriculture and Environment | This course contains one chapter regarding smart nanoparticles, which are functionalized to have some intelligence for site-specific delivery of effective components, food quality indication, and pollution detection, etc. |
| Wildlife Ecology & Conservation | WIS4570C Wildlife Behavior and Conservation | · Lab exercise – AI acoustic software to classify bird sounds via unsupervised classification |
| Advertising | ADV3001 Advertising Strategy | Advertising Strategy provides an overview of the planning process required to develop a successful persuasive marketing communications plan. Case studies and projects teach the skills needed to solve a marketer’s promotions challenges and to engage target audiences. Part of this process involves data analytics and data science. Simmons, Statista, and analytics based on soft data from secondary research is used |
| Advertising | ADV3500 Digital Insights | This course will acquaint students with knowledge and skills of doing research and generate insights in today’s digital world. ADV 3500 will provide information for decision making to solve communication and persuasion problems and issues in different social and cultural contexts. Students will gain an understanding of the ecology of the digital world and culturally diverse society, as well as learn various research methods and analytic tools that could be applied to generate insights and facilitate decision making in such an environment. In some sections, students use AI enabled experiential online learning platform (OCEL.AI) to do research. All students in all sections work with data. |
| Advertising | ADV4300 Media Planning | This course will introduce you to the basic principles of media planning. It will prepare you to understand media terminology, interpret syndicated research, gather audience measurements, conduct competitive analysis, create and evaluate marketing/media objectives and, ultimately, design effective, integrated media plans. The class covers programmatic buying, which has elements of AI. |
| Journalism | JOU3365 AI in Media and Society (new course number effective Fall 2021) | Gain an understanding of artificial intelligence and machine learning as they apply to the media professions, including journalists reporting on AI. Explore major developments in AI technologies as covered by the mass media. Learn to detect hype and exaggeration in descriptions of AI’s promises and potential risks and dangers. Examine use of AI systems in finance, healthcare, hiring decisions, housing, policing, etc. |
| Public Relations | PUR4932 Social Media Analytics | Social media provide a wealth of data that can help organizations better understand and build relationships with their publics. In this three-credit course, students will learn to leverage social media data to draw insights about an organization and its publics, provide actionable, data-driven recommendations and inform social media strategy |
| Telecommunications | MMC3420 Consumer and Audience Analysis | Big data is driving innovation in consumer/audience research. This course is designed to provide students with an overview of the field of consumer analytics; to define key metrics used in consumer and audience analytics; to explore various tools and software used to track analytics |
| Telecommunications | RTV4420 New Media Systems | The main project in this capstone class for the Media and Society sequence is to write a scenario looking at the development of some aspect of the media world in the context of an emerging technology. One of the choices will be AI (for example, students can look at how AI will impact the film/video production environment) |
| Telecommunications | RTV4432 Ethics and Problems in Telecommunications | The course covers questions like: What is AI? How does it influence our decision-making processes? What is the goal of AI for advertisers in social media? What is social media addiction and why is it so prevalent? What responsibilities do educators have in informing their students about the applications of AI, its benefits and its perils? What is the nexus between AI and the disinformation society, i.e., fake news? |
| Telecommunications | RTV4700 Telecommunications Law and Regulation | Focuses on laws surrounding emerging media and technology including artificial intelligence |
| Telecommunications | RTV4800 Telecommunications Planning and Operations | One week of the semester is focused on AI and management/marketing |
| Astronomy | AST4930 Advanced Computational Techniques in Physics and Astonomy | currently with CLAS Curriculum Committee |
| Astronomy | Python for Astronomical and Geological Sciences | currently with CLAS Curriculum Committee |
| Classics | CLA3XXX AI in Antiquity and Today | Examines the origins of artificial intelligence in ancient Greece and compares it to the use and acceptance of AI in modern society. Addresses identity issues related to AI, including gender, racism, and slavery. |
| Classics | CLT3930 Special Topics in Classical Literature: "It's Alive! Pygmalion, Living Statues and Ancient Robots and Modern AI" | |
| Economics | ECO4421 Econometrics | |
| Economics | ECO4422 Econometrics 2 | |
| Gender, Sexuality, and Women's Studies | WST3610 Gender, Race, and Science | |
| Geography | GEO2351 Geographical Sciences and Sustainability | |
| Geography | GEO4169 Spatial Econometrics and Modeling | |
| Geography | GEO4285 Models in Geographic Hydrology | |
| Geography | GEO4167C Intermediate Quantitative Analysis for Geographers | |
| Geography | GEO4306C Geography of Vector-borne Diseases | |
| Geography | GIS2002 The Digital Earth | |
| Geography | GIS3043 Foundations of GIS | |
| Geography | GIS4037 Digital Image Processing | |
| Geography | GIS4113 Intro to Spatial Networks | |
| Geography | GIS4115 Applied Geostats | |
| Geography | GIS4324 GIS Analysis of Hazard Vulnerability | |
| Geography | GIS4500 Population GIS | |
| Geography | GIS2XXX The World and Big Data | |
| Geography | GIS3420C GIS Modeling for Public Health | |
| Geography | GIS4102C GIS Programming | |
| Geography | GIS4324C Applications in GIS for Zoonoses and Disease Ecology | |
| Geography | GIS4XXX GeoAI - Geogrpahic Artificial Intelligence | |
| Geography | MET4410 Radar and Satellite Meteorology | |
| Geography | MET4560 Atmospheric Telecommunications | |
| Geography | MET4750 Spatial Analysis of Atmospheric Data using GIS | |
| History | proposed Digital History Course | |
| Interdisplinary studies | IDS3XXX AI in the Social Sciences | Defines artificial intelligence, big data, and machine learning and discusses the distinctions between these fields. Describes the use of machine learning, search algorithms, text and image analysis, and other AI methodologies to address important social science research questions. Presents artificial intelligence applications to various social sciences disciplines and provides hands-on data analysis experience of the use of AI within the social sciences |
| Philosophy | PHI3641 Ethics and Innovation | |
| Philosophy | PHI3681 Ethics, Data, and Technology | |
| Polictical Science | POS4931 Election Data Science | Second course in the data analytics ISE sequence that focuses on how and why algorithms work using an application-oriented approach. Studies advanced analytical and learning models that enhance decision making by converting data to information. Provides insights into how to choose,the most effective tool for implementing a specific model |
| Polictical Science | POS4932 Applied Political Behavior | Taught by Dr. Hannah Alarian, the course will cover the applied aspects of studying political behavior, including going through IRB, designing and critiquing survey instruments, and introductory data analysis. It will code in R. |
| Psychology | EXP4174C Advanced undergraduate lab course, Sensory Processing (training in R) | |
| Psychology | PSB4342 Intro to Cognitive Neuroscience | |
| Psychology | PSB4343C Advanced undergraduate lab course, Cognitive Neuroscience (EEG methods) | |
| Psychology | PSY4930 Judgment & Decision Making | |
| Writing Center | ENC2305 The Post-human Condition | |
| Writing Center | ENC3254 Writing in the Disciplines | |
| Writing Center | ENC3468 Writing in the Physical Sciences | |
| Architecture | ARC3181 Advanced Topics Digital Architecture | Course module that introduces AI applications and case study examples in the field of Architectural research and practice. Course introduces parametric modeling and algorithms applied to digital modeling and fabrication in architecture. |
| Construction Management | BCN4905 Course_based Undergraduate Reseacrh Experience | |
| Landscape | LAA2330 Site Analysis | Course module that provides an introduction to AI as applied to site analysis and the field of landscape architecture. |
| DCP4XXX AI in the Built Environment | ||
| CISE | CISE4930 GPU Accelerated Computing | Taught by Beverly Sanders |
| CISE | CISE6930 GPU Accelerated Computing | Taught by Beverly Sanders |
| Electrical and Computer Engineering | EEE4773 Fundamentals of Machine Learning | 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. |
| Electrical and Computer Engineering | EEL3872 Fundamentals of Artificial Intelligence | General overview of AI. This is a required course for the AI Undergraduate Certificate called AI Fundamentals and Applications |
| Electrical and Computer Engineering | EEL4930 Data Science for ECE | Analysis, processing, simulation, and reasoning of data. Includes data conditioning and plotting, linear algebra, statistical methods, probability, simulation, and experimental design. |
| Electrical and Computer Engineering | EEL4930 Introduction to Machine Learning | Engineering and hardware concepts pertaining to design of intelligent computer systems. |
| Electrical and Computer Engineering | EEL4665C Intelligent Machines Design Laboratory | Design simulation, fabrication, assembly and testing of intelligent robotic machines. Laboratory. |
| Engineering Education | EEL3872 AI Fundamentals | Required course for undergraduate AI Certificate |
| Industrial and Systems Engineering | ESI4611 Advanced Data Analytics | Second course in the data analytics ISE sequence that focuses on how and why algorithms work using an application-oriented approach. Studies advanced analytical and learning models that enhance decision making by converting data to information. Provides insights into how to choose,the most effective tool for implementing a specific model |
| N/A | NGR4827 & 4815 Leadership and Innovation in Nursing/ Professional Nursing Transformation | |
| Biostatistics | PHC4XXX Data Visualization in the Health Sciences | |
| Dean's Office | PHC3XXX Ethics in Artificial Intelligence: Who’s Protecting Our Health | |
| Epidemiology | PHC3XXX Higher Thinking for Healthy Humans: AI in Healthcare and Public Health | |
| Health Science | HSC4191 Health Informatics and Emerging Healthcare Technologies | |
| Health Services Research Management and Policy | HAS4191 Health Informatics & Emerging Healthcare Technologies | This course provides a fundamental understanding of health informatics, healthcare information systems, and emerging healthcare technologies, starting with the core informatics competencies and the foundation of knowledge model. Key topics will include cognitive science, legal and ethical aspects, HIPAA privacy and security regulations, systems development life cycle, electronic security, electronic health records, patient engagement, community health, telehealth, data mining, IT certifications, evidence-based practice, and translational research. The course will also provide an in-depth look at current technologies to include wearable sensor-based systems for health monitoring and prognosis and the use of mobile health (mHealth) applications in the medical and healthcare sectors to gain an understanding of their emerging role in health informatics. |
| Construction Management | BCN4594 / 6584 Building Energy Modeling | This course builds essential knowledge of building energy and sustainability, and provides necessary background to use building energy simulation software tools. The goal of this course is to use building performance modeling as an investigative tool to improve overall energy efficiency of the building. Use of AI to forecast individual building and campus level energy use are discussed in this course. This is an undergraduate/ graduate course. |
| Digital Worlds | DIG6125C Digital Design and Visualization | |
| Digital Worlds | DIG6125C Digital Design and Visualization | |
| Accounting | ACG7848 Data Analysis and Skills | |
| Accounting | ACG7849 Web Crawling & Text Analysis | |
| ISOM | ISM6251 Programming for Business Analytics | |
| ISOM | ISM6257 Intermediate Business Programming | |
| ISOM | ISM6405 Intro to Business Intelligence | |
| ISOM | ISM6423 Data Mining for Business Intelligence | |
| ISOM | ISM6485 Electronic Commerce and Logistics (capstone course) | |
| ISOM | ISM6562 Business Data Presentation and Visualization | |
| Marketing | MAR6930 Marketing Analytics I | |
| Marketing | MAR6930 Marketing Analytics II | |
| QMB6358 Statistical Analysis for Managerial Decisions | ||
| QMB6755 Managerial Quantitative Analysis | ||
| QMB6845 Supply Chain Analytics: Gaming the Supply Chain | ||
| QMB6930 Analytics Processes for Business Bootcamp | ||
| QMB6930 Analytics Practicum I | ||
| QMB6930 Analytics Practicum II | ||
| QMB6930 Analytics Practicum III | ||
| QMB6XXX Artificial Intelligence Methods in Business | ||
| Agricultural&Biological Engineering | ABE5009 Control Methods in SmartAg Systems | This course covers the convergence of modern control approaches with Artificial Intelligence through the use of Fuzzy Logic, classical Neural Networks and Deep Learning in agricultural systems. |
| Agricultural&Biological Engineering | AOM5435 Advanced Precision Ag | AI introduction on history, current status, and example applications in crop production |
| Agricultural&Biological Engineering | ABE6644 Agricultural Decision Systems | |
| Agricultural&Biological Engineering | ABE6933 Statistical Learning | |
| Agricultural&Biological Engineering | ABE6933 Bayesian Methodology & Applications | |
| Agronomy | AGR6322 Advanced Plant Breeding | includes a 2-period lecture in “Implementing AI and HTP (high-throughput phenotyping) in Plant Breeding)”. |
| Agronomy | PLS6623 Invasion Ecology | includes similar discussion of detecting and forecasting invasions using AI technology, although the depth of discussion depends on student interest. |
| Food Science & Human Nutrition | HUN6936 Nutritional Epigenomics and Metabolic Disease | (1) this course discusses the study of the interactions between nutrients and genes in whole cell, organ, or organism, using high-throughput measurements and bioinformatics tools. |
| Horticultural Sciences | HOS5330 Postharvest Technologies for Horticultural Crops | |
| Horticultural Sciences | HOS6236 Molecular Marker Assisted Plant Breeding | The course includes theory, methods and procedures required to apply DNA molecular information in plant breeding programs. A section of the course includes theory and hands-on labs on the application of different computational algorithms (AI) to predict plant phenotypes and accelerate plant breeding. |
| Horticultural Sciences | HOS6331 Postharvest Biology | |
| Horticultural Sciences | HOS6932 Plant Biochemistry | This course includes at least two topics that involve AI: 1) Protein structure prediction. An example I use, Google recently won a competition for correct prediction of an unknown protein 3D structure using AI. 2) A second application for optimization of metabolic models. |
| Microbiology & Cell Science | MCB6937 Applied Artificial Intelligence in Biological Sciences | mostly statistical machine learning |
| SFRC-FRC | FNR6560 Introduction to Bayesian Statistics in Life Sciences | Relationship to AI: talks about generative models, model selection, probability distributions, importance of uncertainty, Bayesian statistics, etc. |
| SFRC-FRC | FOR6156 Simulation Analysis of Forest Ecosystems. | Sections discussing Artificial Neural Networks, genetic algorithms, and artificial life. Applications generally are in environment and ecology. |
| SFRC-FRC/Wildlife Ecology&Conservation | STA6093 Introduction to Applied Statistics for Agricultural and Life Sciences | Relationship to AI: talks about overfitting, model selection |
| Soil and Water Sciences | AGG6503 Nanotechnology in Food, Agriculture and Environment | This course contains one chapter regarding smart nanoparticles, which are functionalized to have some intelligence for site-specific delivery of effective components, food quality indication, and pollution detection, etc. |
| Soil and Water Sciences | SWS6722 Soil-Landscape Modeling | AI (machine learning) as one of the modules in the course. My research program in pedometrics (quantitative soil science) and landscape analysis has involved AI (machine learning; not deep learning algorithms) for the past 10+ years. |
| Wildlife Ecology & Conservation | WIS5496 Wildlife Research Design | · Introduce students to machine learning (philosophy and a demo) as a major approach to data analysis in ecology (for pattern-finding/confirmation in big data). Readings of reference chapters that contrast machine learning with statistical modelling (frequentist versus Bayesian) and basic Monte Carlo approaches; demonstrate the approach with classification tree analysis. |
| Wildlife Ecology & Conservation | WIS6934 Ecological Forecasting | · Teaching of advanced machine learning -- Artificial intelligence (machine learning) for forecasting the future state of ecological systems |
| Telecommunications | MMC5215 Technology Policy | This course presents an advanced, interdisciplinary discussion of technology law, policy, and regulation. In examining these topics, we will emphasize the intersection of technology, economics, public policy, and human and organizational behavior with a particular emphasis on media technology, including artificial intelligence |
| Telecommunications | MMC6566 Communicating Privacy | This course examines privacy, data governance, and security as they relate to emerging technology, including artificial intelligence, by investigating how organizations, both government, civil society, and others, are finding it difficult to communicate related to data collection and usage. This class also investigates consequences for both individuals and organizations |
| Telecommunications | MMC6660 Mass Comm and Society | to become "Communication, Technology, and Society" Examines the impacts of media and technology, including artificial intelligence, on different aspects of society. |
| Telecommunications | MMC6936 Special Topics--HMC (Human Machine Communication) | Human-machine communication (HMC) involves communication with digital interlocutors including embodied machine communications, virtual/artificially intelligent agents, and technologically augmented persons, either in real or augmented environments. It’s an area of study that investigates the creation of meanings among humans and machines |
| Telecommunications | MMC6936 Special Topics--Computational Methods for Media Research | This course is a project-oriented course with an emphasis on digital media and computational methods. In this course, students will learn how to conduct social research using digital trace data (broadly defined as data collected through digital means) and computational methods (including but not limited to text analysis, machine learning, and social network analysis) |
| Telecommunications | MMC6939 Consumer & Audience Analysis | Big data is driving innovation in consumer/audience research. This course is designed to provide students with an overview of the field of consumer analytics; to define key metrics used in consumer and audience analytics; to explore various tools and software used to track analytics |
| Telecommunications | RTV6508 Audience Analysis | Students learn about artificial intelligence (AI) as it applies to audience and consumer research, its applications, and ethical implications |
| Chemistry | CHM6165 Chemometrics | |
| Economics | ECO5426 Econometric Analysis 1 | |
| Economics | ECO5427 Econometric Analysis 2 | |
| Economics | ECO5435 Econometric Data Analysis | |
| Economics | ECO7415 Statistical Methods in Economics | |
| Economics | ECO7424 Econometric Models and Methods | |
| Economics | ECO7426 Econometric Methods 1 | |
| Economics | ECO7427 Econometric Methods 2 | |
| Economics | ECO7534 Empirical Public Economics 1 | |
| Geography | GEO6161 Intermidiate Quantitative Methods for Geographers | |
| Geography | GEO6166 Adv Quantitative Methods for Spatial Analysis | |
| Geography | GEO6168 Spatial Econometrics and Modeling | |
| Geography | GIS5505 Population GIS | |
| Geography | GIS6104 Spatial Networks | |
| Geography | GIS6117 Applied Geostats | |
| Geography | GIS6325 GIS Analysis of Hazard Vulnerability | |
| Geography | GIS5038C Remote Sensing | |
| Geography | GIS5107C Geographical Information Systems in Research | |
| Geography | GIS6425C GIS Models for Public Health | |
| Geography | GIS6456C Applications of GIS for Zoonoses and Disease Ecology | |
| Geography | MET6168 Spatial Analysis of Atmospheric Data Using GIS | |
| Geography | MET6565 Seminar in Atmospheric Teleconnections | |
| Geography | MET6752 Spatial Analysis of Atmospheric Data Using GIS | |
| Philosophy | PHI5696 Ethics and Emerging Technology | |
| Philosophy | PHI6639 Topics in Ethics of Technology | |
| Philosophy | PHI6698 Bioethics and Biotechnology | |
| Philosophy | PHI6699 Ethics, AI and Data | |
| Psychology | PSY6930 Psychological & Behavioral Modeling | |
| Architecture | ARC6611 Advanced Topics in Architecture Technology | Hurricane Shutter Prototypes: Course project develops design prototypes for hurricane shutter. The project integrates simulation modelling, computational fluid dynamics modelling, structural modelling and visualization, and other algorithms for optimizing surface shapes for resistance to selected performance criteria. |
| Architecture | ARC6311C Building Information Modeling (BIM) | This course addresses the principles of Building Information Modeling (BIM). The course also develops the key concepts of BIM and their relationship to computational design, detailing, and construction. The AI component includes a generative design that explores all the possible permutations of a solution, quickly generating design alternatives. It uses machine learning to test and learn from each iteration what works and what doesn’t. |
| Construction Management | BCN5470 Construction Methods Improvement | Methods of analyzing and evaluating construction techniques to improve project time and cost control. Other important topics, including work sampling, productivity ratings, crew balance studies, time management, and emerging technologies are also included. |
| Construction Management | BCN5905 AI and Machine Learning in Construction | The understanding and application of empirically based artificial intelligence techniques (specifically machine learning and artificial neural networks – ML/ANN) to the field of construction science, technology and management, including: understanding of the areas of application, current limitations and future potential of ML/ANN technologies; broad knowledge of the available ML/ANN models and development algorithms; in-depth understanding of the different ways of representing a problem and interpreting a solution using ML/ANN systems; understanding of the suitability of different ML/ANN systems for different applications, and knowledge of how to decompose a problem into a form suitable for solution using these technologies; understanding and experience in following the basic steps required to develop a valid ML/ANN model; knowledge and experience in the application of state-of-the-art ML/ANN tools, in particular deep learning, transfer learning and reinforcement learning. Course materials make extensive use of examples and case studies from construction and its related disciplines. |
| Construction Management | BCN6905 Advanced BIM | |
| DCP | DCP7794 Doctoral Seminar 4 | Finding doctoral research questions, conceptualizing a doctoral dissertation, writing a dissertation, academic and research job market and associated challenges, conference presentations, and other related topics. The basic utility of AI-based research methods is referenced in the course modules covering scientometric analysis of DCP relevant research and preliminary research topic data analysis. |
| Landscape | LAA4394 Advanced Design Communication | Algorithms; Communication focus; One module out of two introduces parametric approaches in Landscape Architecture design (using Grasshopper plugin for the Rhino 3D modeling and digital fabrication), introduces basic concept of Algorithms in design and construction. |
| Electrical and Computer Engineering | EEE6504 Machine Learning for Time Series | Theory of adaptation with stationary signals; performance measures. LMS, RLS algorithms. Implementation issues and applications. |
| Electrical and Computer Engineering | EEE6512 Image Processing and Computer Vision | Pictorial data representation; feature encoding; spatial filtering; image enhancement; image segmentation; cluster seeking; two-dimensional z-transforms; scene analysis; picture description language; object recognition; pictorial database; interactive graphics; picture understanding machine. |
| Electrical and Computer Engineering | EEE6561 Fundamentals of Biometric Identification | Methods and principles for the automatic identification/authentication of individuals. Technologies include fingerprint, face, and iris biometrics. Additional topics include biometric system design, performance evaluation, multi-modal biometric systems, and biometric system security. |
| Electrical and Computer Engineering | EEE6586 Automatic Speech Processing | Various models of speech production and perception. Operation of speech synthesizers. Discussion of speech recognition. Mathematical models of speech signals. |
| Electrical and Computer Engineering | EEL5406 Computational Photography | Fundamentals of computational photography, sensing, imaging and illumination. |
| Electrical and Computer Engineering | EEL5840 Fundamentals of Machine Learning | Engineering and hardware concepts pertaining to design of intelligent computer systems. |
| Electrical and Computer Engineering | EEL5934 Control of Marine and Aerial Vehicles | This course introduces the basic theory and practical aspects of control aquatic and aerial vehicles. The first part of the course will cover basic materials while the later part of the course introduces a selective introduction to the state-of-the-art research problems currently under investigation. |
| Electrical and Computer Engineering | EEL6532 Information Theory | Applications of information theory to communications and other releated areas. |
| Electrical and Computer Engineering | EEL6533 Data Analytics and Decision Sciences | Hypothesis testing of signals in the presence of noise by Bayes, Neyman-Pearson, minimax criteria; estimation of signal parameters. |
| Electrical and Computer Engineering | EEL6814 Neural Networks and Deep Learning | Nonlinear modeling and neural networks. Gradient descent learning in the additive neural model; statistical learning concepts; dynamic neural networks, function approximation and short-term memory; unsupervised learning networks; generative models and statistical representation; autonomous learning using cognitive principles. Importance and challenges of deep learning; applications for image, video, speech recognition. |
| Electrical and Computer Engineering | EEL6825 Pattern Recognition and Intelligent Systems | Decision functions; optimum decision criteria; training algorithms; unsupervised learning; feature extraction, data reduction; potential functions; syntactic pattern description; recognition grammars; machine intelligence. |
| Electrical and Computer Engineering | EEL6841 Machine Intelligence and Synthesis | Theory of machine intelligence applied to general problem of engineering intelligent computer systems and architecture. Applications emphasized. |
| Electrical and Computer Engineering | EEL6935 Machine Learning for Natural Language Processing | The goal of natural language processing is to allow machines to understand and process human language. This course extends the knowledge presented in EEL-5840 Elements of Machine Intelligence to understand how machine learning methods can be applied to natural language processing. During the first part of the course, fundamental concepts and methods used in natural language processing are introduced. During the second portion of the course, more recent machine learning-based approaches, particularly neural networks/deep-learning are presented. |
| Electrical and Computer Engineering | EEL6935 Stochastic Control | The first goal is to learn how to formulate models for the purposes of control, in applications ranging from finance to power systems to medicine. Linear and Markov models are chosen to capture essential dynamics and uncertainty. The course will provide several approaches to design control laws based on these models, and methods to approximate the performance of the controlled system. In parallel with these algorithmic objectives, students will be provided with an introduction to basic dynamic programming theory, closely related stability theory for Markovian1 and linear systems, and simulation and stochastic approximation concepts underlying reinforcement learning. |
| Electrical and Computer Engineering | EEL6935 Stochastic Methods 2 | Basic concepts of random processes; linear systems with random inputs; Markov processes; spectral analysis; applications to systems engineering. |
| Electrical and Computer Engineering | EEL6935 Quantum Computing and Artificial Intelligence | This course offers an introduction to the interdisciplinary fields of quantum computation and quantum AI. The focus will lie on an accessible introduction to the elementary concepts of quantum mechanics, followed by a comparison between computer science and information science in the quantum domain. The theoretical capability of quantum computers will be illustrated by analyzing fundamental algorithms of quantum computation and its potential applications in AI |
| Electrical and Computer Engineering | EEL5666C Intelligent Machines Design Laboratory | Design simulation, fabrication, assembly, and testing of intelligent robotic machines. |
| Pharmaceutical Outcomes and Policy (POP) | PHA6268 Pharmacoepidemiology & Drug Safety | Currently, we have 3 weeks covering introduction to machine learning methods in pharmaceutical outcomes research fields |
| Epidemiology | PHC7065 Critical Skills in Epidemiological Data Management | |
| Epidemiology | PHC7083 Computational Data Science for Epidemiology | |
| Epidemiology | PHC7199 Topics in Precision Medicine and Public Health Informatics | |
| Epidemiology/HOBI | GMS-PHCpending Causal AI for Health Research | |
| Law | 6930 Artificial Intelligence, Technology, and Law | |
| Law | 6930 Artificial Intelligence & Tax Law: Theory and Practice | |
| Law | 6825 Electronic Discovery | |
| Law | 6930 Big Data and the Law | |
| Law | 6930 Cybersecurity and Cybercrimes | |
| Law | 6930 Biotechnology and Medical AI | |
| Law | 6930 Artificial Intelligence and Litigation Strategies | |
| Art and Art History | ART3959 Video | we will talk about AI with respect to surveillance and the ability to edit video using AI algorithms |
| SFRC-GEO | Remote Sensing | will include some portion / discussion on machine learning |
| Pharmacotherapy & Translational Research | PHAPending Principles of Pharmacy Informatics | This course is designed to introduce the theory and concepts of pharmacy informatics and health informatics. It offers a platform for students who are interested in pharmacy informatics career. Students will develop the knowledge to discuss the nature of health informatics as an important discipline; and recognize different methods used to analyze fundamentals of workflow process analysis, system redesign, usability, and human factors. This course will use a combination of lectures, assignments, and final project. |
| All COP departments | PHAPending Introduction to Artificial Intelligence in Pharmacy | This course is designed to introduce the basic concepts of AI/ML methods used in pharmacy research and practice. |
| Tourism, Hospitality & Event Management | HFT4446C GIS & Spatial Analysis for Tourism and Social Data | The course focuses on building spatial data analysis skills using tourism, hospitality, event, destination management, and natural resources data. Combining lecture and lab instruction, the course teaches how to utilize the opportunities provided by dynamically developing methods of geographical information systems (GIS) for visualization and geographic analysis of the data. The 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. |
| Tourism, Hospitality & Event Management | HFT4746 Smart Cities, Attractions, and Theme Parks | The course focuses on building students’ data analysis skills using “real life” data from tourism, leisure and well-being, hospitality, events, sports, and related fields. Combining lecture and lab instruction, the course teaches advanced statistical techniques used in social research to analyze data in order to inform managerial decisions. The course provides an overview of several data analytics methods and emphasizes differences between the methods, application of these methods to practical problems, reporting of findings, and interpretation of results. The methods include factorial ANOVA, MANOVA, exploratory factor analysis, cluster analysis, multiple regression, and introduction to structural equation modeling (path analysis and confirmatory factor analysis). |
| Tourism, Hospitality & Event Management | HFT4442 AI Revolutions and Applications in Tourism, Hospitality, and Events | Examination of the various components of the artificial intelligence revolutions and applications, tourism industry, motivators to travel, and the various market segments. Includes the analyses of the economic, social, cultural, and environmental impacts to AI revolution and applications in tourism, hospitality, event, and theme parks. |
| Tourism, Hospitality & Event Management | LEI4930 Data Mining with Social Data | Smart cities and AI destinations/attractions look across every aspect of operations and use technology to improve outcomes. Visionary leaders and scholars in tourism, hospitality, and event fields have long envisioned smart cities and AI destinations/attractions of the future where residents and visitors thrive. Fine-tuned and seamlessly operated, these artificial intelligent environments hum with advanced multi-modal transit systems, self-sustaining energy grids, clean and safe neighborhoods, integrated services, and meaningful amenities. While incremental progress has been made toward this bright future, cities, communities, destinations, and attractions continue to face complex challenges, including infrastructure upkeep, population growth and migration, risk preventions, crisis management, and sustainability issues. |
| Tourism, Hospitality & Event Management | HMG6448c GIS & Spatial Analysis for Tourism and Social Data | The course focuses on building spatial data analysis skills using tourism, hospitality, event, destination management, and natural resources data. Combining lecture and lab instruction, the course teaches how to utilize the opportunities provided by dynamically developing methods of geographical information systems (GIS) for visualization and geographic analysis of the data. The 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. |
| Tourism, Hospitality & Event Management | HMG6740 Smart Cities, Attractions, and Theme Parks | The course focuses on building students’ data analysis skills using “real life” data from tourism, leisure and well-being, hospitality, events, sports, and related fields. Combining lecture and lab instruction, the course teaches advanced statistical techniques used in social research to analyze data in order to inform managerial decisions. The course provides an overview of several data analytics methods and emphasizes differences between the methods, application of these methods to practical problems, reporting of findings, and interpretation of results. The methods include factorial ANOVA, MANOVA, exploratory factor analysis, cluster analysis, multiple regression, and introduction to structural equation modeling (path analysis and confirmatory factor analysis). |
| Tourism, Hospitality & Event Management | HMG6440 AI Revolutions and Applications in Tourism, Hospitality, and Events | Examination of the various components of the artificial intelligence revolutions and applications, tourism industry, motivators to travel, and the various market segments. Includes the analyses of the economic, social, cultural, and environmental impacts to AI revolution and applications in tourism, hospitality, event, and theme parks. |
| Tourism, Hospitality & Event Management | HMG6583c Data Mining with Social Data | Smart cities and AI destinations/attractions look across every aspect of operations and use technology to improve outcomes. Visionary leaders and scholars in tourism, hospitality, and event fields have long envisioned smart cities and AI destinations/attractions of the future where residents and visitors thrive. Fine-tuned and seamlessly operated, these artificial intelligent environments hum with advanced multi-modal transit systems, self-sustaining energy grids, clean and safe neighborhoods, integrated services, and meaningful amenities. While incremental progress has been made toward this bright future, cities, communities, destinations, and attractions continue to face complex challenges, including infrastructure upkeep, population growth and migration, risk preventions, crisis management, and sustainability issues. |
| Applied Physiology & Kinesiology | PET5936 Applied Data Science & Analtyics in Human Performance | Examines fundamental concepts related to the acquisition, analysis, and interpretation of data relevant to the outcome of human performance across myriad physical and cognitive domains including sport, exercise, tactical operations, and medical professions. Addresses the use of statistics and broader fields of data science, artificial intelligence, analytics, and technology management necessary to evaluate performance and strategically adjust training methods to enhance human performance, health, and well-being. Content will aid students preparing to sit for the NSCA Certified Performance and Sport Scientist (CPSS) exam. |
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