AI Learning at UArizona
Find out about Programs and Classes here at the university
Discover Artificial Intelligence Courses
Artificial intelligence is incorporated into disciplines and courses across the curriculum, from the health, physical and social sciences to engineering, business, law, and beyond.
College of Applied Science and Technology
Department: Humanities and Science Units 3
The class is an introduction to Artificial Intelligence from a computer science perspective. Emphasis of the course is knowledge representation and reasoning techniques in the design and implementation of intelligent systems. Topics include problem formulation, problem solving and search, knowledge based systems and inference, and machine learning. Students are expected to identify and analyze real problems in the world around us that might benefit from AI and to design and implement possible solutions.
Cyber Operations program Units 3
This course will lay a foundation for students to understand how to process, analyze, and visualize data. Topics include data collection and integration, exploratory data analysis, statistical inference and modeling, machine learning, and data visualization. The emphasis of the course topics will be placed on integration and synthesis of concepts and their application to solving problems. Students will explore these topics using software tools.
College of Engineering
Department: Biomedical Engineering Units 3
The practice of modern medicine in a highly regulated, complex, sociotechnical enterprise is a testament to the future healthcare system where the balance between human intelligence and artificial expertise will be at stake. The goal of this course is to introduce the underlying concepts, methods, and the potential of intelligent systems in medicine. We will explore foundational methods in artificial intelligence (AI) with greater emphasis on machine learning and knowledge representation and reasoning, and apply them to specific areas in medicine and healthcare including, but not limited to, clinical risk stratification, phenotype and biomarker discovery, time series analysis of physiological data, disease progression modeling, and patient outcome prediction. As a research and project-based course, student(s) will have opportunities to identify and specialize in particular AI methods, clinical/healthcare applications, and relevant tools.
Department: Systems and Industrial Engineering Units 3
The practice of modern medicine in a highly regulated, complex, sociotechnical enterprise is a testament to the future healthcare system where the balance between human intelligence and artificial expertise will be at stake. The goal of this course is to introduce the underlying concepts, methods, and the potential of intelligent systems in medicine. We will explore foundational methods in artificial intelligence (AI) with greater emphasis on machine learning and knowledge representation and reasoning, and apply them to specific areas in medicine and healthcare including, but not limited to, clinical risk stratification, phenotype and biomarker discovery, time series analysis of physiological data, disease progression modeling, and patient outcome prediction. As a research and project-based course, student(s) will have opportunities to identify and specialize in particular AI methods, clinical/healthcare applications, and relevant tools.
Department: Systems and Industrial Engineering Units 3
We consider optimization problems whose objective functions are unknown and hence have to be learned from data. Such problems are pervasive in science and industry, e.g., when
- designing prototypes in engineering,
- automated tuning of machine learning algorithms, e.g., in deep learning,
- optimizing control policies in robotics,
- developing pharmaceutical drugs, and many more.
Bayesian optimization methods are popular in the machine learning community due to their high sample-efficiency and have become a key technique in the area of \"automatic machine learning\". We introduce a general framework in which to understand and formulate such optimal learning problems, and provide a survey of problems, methods, and theoretical results.
Department: Electrical & Computer Engr Units 3
Machine learning deals with the automated classification, identification, and/or characterizations of an unknown system and its parameters. There are an overwhelming number of application driven fields that can benefit from machine learning techniques. This course will introduce you to machine learning and develop core principles that allow you to determine which algorithm to use, or design a novel approach to solving to engineering task at hand. This course will also use software technology to supplement the theory learned in the class with applications using real-world data.
Department: Systems and Industrial Engineering Units 3
This course will provide senior undergraduate and graduate students from a diverse engineering disciplines with fundamental concepts, principles and tools to extract and generalize knowledge from data. Students will acquire an integrated set of skills spanning data processing, statistics and machine learning, along with a good understanding of the synthesis of these skills and their applications to solving problem. The course is composed of a systematic introduction of the fundamental topics of data science study, including: (1) principles of data processing and representation, (2) theoretical basis and advances in data science, (3) modeling and algorithms, and (4) evaluation mechanisms. The emphasis in the treatment of these topics will be given to the breadth, rather than the depth. Real-world engineering problems and data will be used as examples to illustrate and demonstrate the advantages and disadvantages of different algorithms and compare their effectiveness as well as efficiency, and help students to understand and identify the circumstances under which the algorithms are most appropriate.
Department: Systems and Industrial Engineering Units 3
This course will provide senior undergraduate and graduate students from a diverse engineering disciplines with fundamental concepts, principles and tools to extract and generalize knowledge from data. Students will acquire an integrated set of skills spanning data processing, statistics and machine learning, along with a good understanding of the synthesis of these skills and their applications to solving problem. The course is composed of a systematic introduction of the fundamental topics of data science study, including: (1) principles of data processing and representation, (2) theoretical basis and advances in data science, (3) modeling and algorithms, and (4) evaluation mechanisms. The emphasis in the treatment of these topics will be given to the breadth, rather than the depth. Real-world engineering problems and data will be used as examples to illustrate and demonstrate the advantages and disadvantages of different algorithms and compare their effectiveness as well as efficiency, and help students to understand and identify the circumstances under which the algorithms are most appropriate.
Department: Aerospace and Mechanical Engineering Department Units 3
This is another course on machine learning and AI with a specific focus on robotics and aerospace applications.
Department: Mining geology and geophysical engineering Units 3
Recent advances in technology have enable mining companies with the ability to collect in near real-time large amounts of data. Machine learning represents a valuable set of tools to extract valuable information, synthetize predictive models, and overall contribute to a better understanding of many aspects of the mining operations. This class provides formal introduction to machine learning topics, with a specific emphasis in applications associated to the mining and minerals industry. First, a general overview of the machine learning methodology is provided, along with a description of the main architectures used in current applications. Emphasis is given to tasks such as data characterization, model selection and tunning, performance metrics, and validation strategies. The second part of the class is project-based and focused specifically on the development of a machine learning application for mining or geology problems.
Department: Mining geology and geophysical engineering Units 3
Recent advances in technology have enable mining companies with the ability to collect in near real-time large amounts of data. Machine learning represents a valuable set of tools to extract valuable information, synthetize predictive models, and overall contribute to a better understanding of many aspects of the mining operations. This class provides formal introduction to machine learning topics, with a specific emphasis in applications associated to the mining and minerals industry. First, a general overview of the machine learning methodology is provided, along with a description of the main architectures used in current applications. Emphasis is given to tasks such as data characterization, model selection and tunning, performance metrics, and validation strategies. The second part of the class is project-based and focused specifically on the development of a machine learning application for mining or geology problems.
Department: Electrical & Computer Engr Units 3
Provides an introduction to problems and techniques of artificial intelligence (AI). Automated problem solving, methods and techniques; search and game strategies, knowledge representation using predicate logic; structured representations of knowledge; automatic theorem proving, system entity structures, frames and scripts; robotic planning; expert systems; implementing AI systems.
Department: Electrical & Computer Engr Units 3
Provides an introduction to problems and techniques of artificial intelligence (AI). Automated problem solving, methods and techniques; search and game strategies, knowledge representation using predicate logic; structured representations of knowledge; automatic theorem proving, system entity structures, frames and scripts; robotic planning; expert systems; implementing AI systems. Graduate-level requirements include additional assignments.
Department: Systems and Industrial Engineering Units 3
The increased connectivity due to the rise of the Internet and the growth of smart connected devices (Internet of Things) has brought rapid changes in cyber physical systems operational in many areas including manufacturing, healthcare, transportation, power system, home automation, etc. These changes, dubbed as the Fourth Industrial Revolution (4IR), amalgamate artificial intelligence, advanced robotics, smart sensors, and communication networks, blurring lines between the physical, digital, and biological worlds to automate industrial processes. At the forefront of this revolution are connected Industrial Control Systems (ICS), a group of control systems and associated instrumentation, which include the devices, systems, networks, and controls used to operate and/or automate industrial processes. As ICS and automation become more critical components of our digital world, it will be critical for engineers to know to design, develop, control, and manage these ICS. Moreover, the engineers will not just design and develop optimized ICS; they will also have to ensure their security from cyberattacks.
Department: Systems and Industrial Engineering Units 3
The increased connectivity due to the rise of the Internet and the growth of smart connected devices (Internet of Things) has brought rapid changes in cyber physical systems operational in many areas including manufacturing, healthcare, transportation, power system, home automation, etc. These changes, dubbed as the Fourth Industrial Revolution (4IR), amalgamate artificial intelligence, advanced robotics, smart sensors, and communication networks, blurring lines between the physical, digital, and biological worlds to automate industrial processes. At the forefront of this revolution are connected Industrial Control Systems (ICS), a group of control systems and associated instrumentation, which include the devices, systems, networks, and controls used to operate and/or automate industrial processes. As ICS and automation become more critical components of our digital world, it will be critical for engineers to know to design, develop, control, and manage these ICS. Moreover, the engineers will not just design and develop optimized ICS; they will also have to ensure their security from cyberattacks.
College of Health Sciences
Department: Biomedical Engineering Units 3
This course offers undergraduate and graduate students an in-depth exploration of artificial intelligence (AI) in health care, blending theoretical knowledge with hands-on experience. The course will be held in the university’s state-of-the-art simulation facility, the Arizona Simulation Technology and Education Center (ASTEC), and each class session will have a practicum, hands-on component for immersive learning, ranging from foundational AI principles to its applications in diagnostics, treatment and patient care. Students will gain insights into the transformative role of AI in modern medicine. The overarching objective of the course is to provide students with a comprehensive, working understanding of the principles, applications, challenges and ethical considerations of artificial intelligence in healthcare and help consider how the tools of AI and its methodologies can be used to make health safer, more responsible and more accessible. Students may also get to watch some of the ongoing development of projects with the Artificial Intelligence Division in Simulation, Education and Training housed in the facility.
College of Information Science
College School of Information Units 3
Game development is a vast field with many advanced concepts. This course aims to teach students such concepts, techniques and mechanisms in Unity, covering procedural content generation, design patterns, artificial intelligence, shaders and postprocessing effects, animation, custom interactions and gestures, and performance optimization. The students are expected to have fundamental game development knowledge in Unity and C#. The course is heavily hands-on and project oriented. Students will implement the covered concepts on small-scaled Unity project templates using C# and also develop a larger-scaled final term project. At the end of the course, students will have gained advanced game development skills that can be applied to future jobs or self-development.
College School of Information Units 3
Game development is a vast field with many advanced concepts. This course aims to teach students such concepts, techniques and mechanisms in Unity, covering procedural content generation, design patterns, artificial intelligence, shaders and postprocessing effects, animation, custom interactions and gestures, and performance optimization. The students are expected to have fundamental game development knowledge in Unity and C#. The course is heavily hands-on and project oriented. Students will implement the covered concepts on small-scaled Unity project templates using C# and also develop a larger-scaled final term project. At the end of the course, students will have gained advanced game development skills that can be applied to future jobs or self-development.
College School of Information Units 3
This course explores advanced Machine Learning concepts and theories for learners who have already developed a fundamental understanding of basic methods for pattern recognition and have interest in applied work across contexts and disciplines. This course will advance students' knowledge of machine learning algorithms, neural networks, and a range of deep learning tools, as well as advanced clustering applications and related topics. Students will read and discuss contemporary research from top-tier machine learning conferences and will engage in advanced projects that rely on data to improve system performance.
College School of Information Units 3
This course explores advanced Machine Learning concepts and theories for learners who have already developed a fundamental understanding of basic methods for pattern recognition and have interest in applied work across contexts and disciplines. This course will advance students' knowledge of machine learning algorithms, neural networks, and a range of deep learning tools, as well as advanced clustering applications and related topics. Students will read and discuss contemporary research from top-tier machine learning conferences and will engage in advanced projects that rely on data to improve system performance.
College School of Information Units 3
Algorithms is a crucial component of game development. This course will provide students with an in-depth introduction to algorithm concepts for game development. The course will cover basic algorithm and data structures concepts, basic math concepts related to game algorithms, physics and artificial intelligence based game algorithms that are supplemented with modern examples. Unity Game Engine along with C# programming language will be used throughout the class.
College School of Information Units 3
This course covers important algorithms useful for natural language processing (NLP), including distributional similarity algorithms such as word embeddings, recurrent and recursive neural networks (NN), probabilistic graphical models useful for sequence prediction, and parsing algorithms such as shift-reduce. This course will focus on the algorithms that underlie NLP, rather than the application of NLP to various problem domains.
College School of Information Units 3
The methods and tools of Artificial Intelligence used to provide systems with the ability to autonomously problem solve and reason with uncertain information. Topics include: problem solving (search spaces, uninformed and informed search, games, constraint satisfaction), principles of knowledge representation and reasoning (propositional and first-order logic, logical inference, planning), and representing and reasoning with uncertainty (Bayesian networks, probabilistic inference, decision theory).
College School of Information Units 3
The methods and tools of Artificial Intelligence used to provide systems with the ability to autonomously problem solve and reason with uncertain information. Topics include: problem solving (search spaces, uninformed and informed search, games, constraint satisfaction), principles of knowledge representation and reasoning (propositional and first-order logic, logical inference, planning), and representing and reasoning with uncertainty (Bayesian networks, probabilistic inference, decision theory). Graduate-level requirements include additional reading of supplementary material, more rigorous tests and homework assignments, and a more sophisticated course project.sophisticated application and technique.
College School of Information Units 3
This course explores ethical challenges stemming from data-driven decision making in society. Students will focus on important topics like bias, fairness, privacy, surveillance, discrimination, as well as data collection, storage, and management. Exploring dilemmas tied to data science, artificial intelligence, robotics, etc. will allow students to consider their own data behaviors as well as trends and problems across contexts like organizations, social media, health, and education. Special attention in the class will be given to matters of policy and governing protocols around the world. Related challenges tied to Internet governance, misinformation, fake video, automation, etc., will also be explored.
College School of Information Units 3
This course explores ethical challenges stemming from data-driven decision making in society. Students will focus on important topics like bias, fairness, privacy, surveillance, discrimination, as well as data collection, storage, and management. Exploring dilemmas tied to data science, artificial intelligence, robotics, etc. will allow students to consider their own data behaviors as well as trends and problems across contexts like organizations, social media, health, and education. Special attention in the class will be given to matters of policy and governing protocols around the world. Related challenges tied to Internet governance, misinformation, fake video, automation, etc., will also be explored.
College School of Information Units 3
This course will introduce students to the theory and practice of data mining for knowledge discovery. This includes methods developed in the fields of statistics, large-scale data analytics, machine learning and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Topics include understanding varieties of data, classification, association rule analysis, cluster analysis, and anomaly detection. We will use software packages for data mining, explaining the underlying algorithms and their use and limitations. The course include laboratory exercises, with data mining case studies using data from biological sequences and networks, social networks, linguistics, ecology, geo-spatial applications, marketing and psychology.
College School of Information Units 3
This course will introduce students to the concepts and techniques of data mining for knowledge discovery. It includes methods developed in the fields of statistics, large-scale data analytics, machine learning, pattern recognition, database technology and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Topics include understanding varieties of data, data preprocessing, classification, association and correlation rule analysis, cluster analysis, outlier detection, and data mining trends and research frontiers. We will use software packages for data mining, explaining the underlying algorithms and their use and limitations. The course include laboratory exercises, with data mining case studies using data from many different resources such as social networks, linguistics, geo-spatial applications, marketing and/or psychology
College School of Information Units 3
This course provides an in-depth understanding of artificial intelligence in digital games (Game AI), focusing on developing agents using classical and machine learning approaches. The course will cover the design space of Game AI, examining issues such as observability, stochasticity, and time granularity. Core algorithms include agent movement, decision-making, and high-level gameplay strategy. In addition, students will explore nontraditional applications of Game AI, such as agent animation, evolution, and social simulation. This course is projects-oriented and will include numerous examples of Game AI in practice. A basic understanding of game algorithms is recommended but not required for success in the course.
College School of Information Units 3
This course provides an in-depth understanding of artificial intelligence in digital games (Game AI), focusing on developing agents using classical and machine learning approaches. The course will cover the design space of Game AI, examining issues such as observability, stochasticity, and time granularity. Core algorithms include agent movement, decision-making, and high-level gameplay strategy. In addition, students will explore nontraditional applications of Game AI, such as agent animation, evolution, and social simulation. This course is projects-oriented and will include numerous examples of Game AI in practice. A basic understanding of game algorithms is recommended but not required for success in the course.
College School of Information Units 3
While the 20th Century saw the rise of the knowledge worker and the information worker, the 21st Century has ushered in the era of the creative professional. Our society is being rapidly transformed by new technologies that are revolutionizing many spheres of life, from entrepreneurship to artistic production. This course provides an introduction to software and hardware packages that are spurring innovation and creativity. Students will explore rapid prototyping, object design, and physical computing using Computer-Aided Design Software, 3D printing technology, and Arduino circuit boards. The Processing programming language will be introduced in this course and used to create generative artworks in both visual and audio idioms. An overview of creative evolutionary computation will survey applications of genetic algorithms and artificial intelligence for creating art.
College School of Information Units 3
Machine learning describes the development of algorithms which can modify their internal parameters (i.e., \"learn\") to recognize patterns and make decisions based on example data. These examples can be provided by a human, or they can be gathered automatically as part of the learning algorithm itself. This course will introduce the fundamentals of machine learning, will describe how to implement several practical methods for pattern recognition, feature selection, clustering, and decision making for reward maximization, and will provide a foundation for the development of new machine learning algorithms.
College School of Information Units 3
Neural networks are a branch of machine learning that combines a large number of simple computational units to allow computers to learn from and generalize over complex patterns in data. Students in this course will learn how to train and optimize feed forward, convolutional, and recurrent neural networks for tasks such as text classification, image recognition, and game playing.
College School of Information Units 3
Neural networks are a branch of machine learning that combines a large number of simple computational units to allow computers to learn from and generalize over complex patterns in data. Students in this course will learn how to train and optimize feed forward, convolutional, and recurrent neural networks for tasks such as text classification, image recognition, and game playing.
College School of Information Units 3
ISTA 331 explores the ideas and techniques that businesspersons and scientists alike use to exploit data in order to create knowledge and make money. Topics and projects may include recommender systems (which powered Amazon's rise to global retail dominance), spam filters (the first machine learning application that affected our daily lives), topic extraction from documents, and an introduction to neural networks.
College of Medicine
Department: Physiology Units 2
Dive into the cutting-edge intersection of Artificial Intelligence (AI) and physiology in our dynamic, 2-unit colloquium designed for those eager to explore how AI is transforming physiology and medicine. This course will explore the role of AI tools across various facets of the healthcare industry — from enhancing educational methodologies to optimizing patient care and improving healthcare operations. Students will explore a range of groundbreaking topics, including AI's application in medical diagnostics, healthcare education/training, its impact on the drug discovery process, and how it's shaping approaches to patient treatment plans. Through a blend of discussions, demonstrations, case studies, and hands-on projects, students will gain firsthand knowledge of AI's potential to address complex challenges in the fields of physiology and medicine/healthcare.
College of Nursing
Department: Nursing Units 3
The intended audience for NURS 648 is the student who is advancing their knowledge of health informatics through a substantive focus in informatics for PhD and as a future leader in health informatics seeking a DNP with Informatics specialty. This course focuses on the theoretical basis of healthcare informatics with an emphasis on management and processing of healthcare data, information, and knowledge. This course is guided by communication theory and will address the following: models used in healthcare informatics, healthcare technologies, human-computer interaction, and database design, artificial intelligence and machine learning, technology guided decision-making, predictive analytics, national issues and policy implications and continued advancements in healthcare technology.
College of Science
Department: Mathematics Units 3
Department: Computer Science Units 3
The goal of this graduate seminar course is to learn more about research in the general field of artificial intelligence. In this course, we will read and review research papers on artificial intelligence. We will also learn how to do research in computer science by reading, evaluating, presenting, and conducting a research project in artificial intelligence. Specific topics to be determined by current literature and faculty and student interest.
Department: Mathematics Units 3
Selected topics from modern statistics and data science. Content varies. The primary purpose of the course is to provide students the opportunity to gain knowledge, experience, and exposure to topics in modern statistics and data science beyond what is presented in the core subjects required for the major.
Department: Astronomy Units 2
This is a graduate level elective course aiming at providing the interface between astronomical data analysis problems and modern statistics methods. Modern astronomy and astrophysics is undergoing a revolution with dramatic increases in both the volume and complexity of astronomical data. The last decade saw the emergence of many terabyte-level sky surveys across the electromagnetic spectrum; the next decade, data volumes will enter the petabyte regime, with an ever strong time domain component. These new data sets represent quantum leaps in our abilities for new astronomical discoveries, but also present significant challenges to standard analysis tools normally employed in astronomy.
The goal of this course is to bridge the gap between modern large data surveys and the data analysis tools that have been provided in normal graduate courses. The course will start with a brief review of the modern statistics framework relevant to large scale data analysis, including probabilities and statistical distribution, classical and Bayesian statistical inferences. Then it will cover the main topics of the course: data mining and machine learning, including density estimation, clustering analysis, dimensionality reduction, regression and model fitting, classification and time series analysis. Another key component of the course is to introduce commonly used data mining and machine learning tools, in the context of Python-based packages, which will be used in solving data problems throughout the course.
Department: Mathematics Units 3
The course teaches students fundamentals of machine learning, covering theoretical principles, statistical machine learning methods and tools, computation algorithms, and their applications to real world problems. Topics include supervised learning (linear and logistic regression, regularization methods such as lasso and ridge, variable decision trees, support vector machines, bagging and boosting, neural networks, and deep learning), unsupervised learning (principle component analysis, clustering, dimension reduction). Important concepts such as bias-variance tradeoff, overfitting, curse of dimensionality, and cross validation are also covered.
Department: Computer Science Units 3
Students will learn how and when machine learning is possible/impossible as well as various algorithms with theoretical guarantees under minimal assumptions. Specifically, the course offers formulation of learning environments (e.g., stochastic and adversarial worlds with possibly limited feedback), fundamental limits of learning in these environments, various algorithms concerning sample efficiency, computational efficiency, and generality. Throughout, students will not only learn fundamental tools upholding the current understanding of machine learning systems in the research community but also develop skills of adapting these techniques to their own research needs such as developing new algorithms for large-scale, data-driven applications.
Department: Computer Science Units 3
Interdisciplinary problems lying at the interface of philosophy and artificial intelligence.
Department: Computer Science Units 3
Interdisciplinary problems lying at the interface of philosophy and artificial intelligence. Courses for which students receive the grade of P (Pass) do not satisfy requirements for the M.A. or Ph.D. or minor in philosophy. Graduate-level requirements include an in-depth research paper on a central theme or topic of the course.
Department: Computer Science Units 3
The course introduces students to principles of data science that are necessary for computer scientists to make effective decisions in their professional careers. A number of computer science sub-disciplines now rely on data collection and analysis. For example, computer systems are now complicated enough that comparing the execution performance of two different programs becomes a statistical estimation problem rather than a deterministic computation. This course teaches students the basic principles of how to properly collect and process data sources in order to derive appropriate conclusions from them. The course has three main components: data analysis, machine learning, and a project where students apply the concepts discussed in class to a substantial open-ended problem.
Department: Computer Science Units 3
Students will learn why machine learning is a fundamentally different way of writing computer programs from traditional programming, and why this is often an attractive way of solving practical problems. Machine learning is all about automatic ways for computers to collect and/or adapt to data to make better predictions and decisions or gain insight; students will learn both advantages and unique risks that this approach offers. They will learn the fundamental frameworks, computational methods, and algorithms that underlie current machine learning practice, and how to derive and implement many of them.
Department: Mathematics Units 3
Basic statistical principles and theory for modern machine learning, high dimensional data analysis, parametric and nonparametric methods, sparse analysis, shrinkage methods, variable selection, model assessment, model averaging, kernel methods, and unsupervised learning.
College of Social & Behavioral Sciences
Department: Linguistics Units 3
This course focuses on statistical approaches to pattern classification and applications of natural language processing to real-world problems
Department: Linguistics Units 3
This class serves as an introduction to human language technology (HLT), an emerging interdisciplinary field that encompasses most subdisciplines of linguistics, as well as computational linguistics, natural language processing, computer science, artificial intelligence, psychology, philosophy, mathematics, and statistics.
Content includes a combination of theoretical and applied topics such as (but not limited to) tokenization across languages, n-grams, word representations, basic probability theory, introductory programming, and version control.
Department: Philosophy Units 3
Interdisciplinary problems lying at the interface of philosophy and artificial intelligence.
Department: Philosophy Units 3
Interdisciplinary problems lying at the interface of philosophy and artificial intelligence. Courses for which students receive the grade of P (Pass) do not satisfy requirements for the M.A. or Ph.D. or minor in philosophy. Graduate-level requirements include an in-depth research paper on a central theme or topic of the course.
Department: Philosophy Units 3
Interdisciplinary problems lying at the interface of philosophy and artificial intelligence. Courses for which students receive the grade of P (Pass) do not satisfy requirements for the M.A. or Ph.D. or minor in philosophy. Graduate-level requirements include an in-depth research paper on a central theme or topic of the course.
Department: Linguistics Units 3
This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models.
Department: Linguistics Units 3
This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models. Graduate-level requirements include assignments of greater scope than undergraduate assignments. In addition to being more in-depth, graduate assignments are typically longer and additional readings are required.
Eller College of Management
Department: Management Information Systems Units 3
This course is to help master-level graduate students develop necessary skills of collecting, storing and managing, exploring, processing and computing big data for business purposes. Topics covered in this course will include big data collection for business, data management with SQL and NoSQL based technologies, data exploration and preprocessing for analytics, data dashboards for business, distributed data storage and computing, and big data based machine learning systems. This course will use state-of-the-art data management, data exploration and computing, and big data machine learning software tools (such as SQL Server, MongoDB, PySpark and TensorFlow) to provide hands-on experience. Students will learn how to apply big data techniques to sift through large amounts of data and provide actionable business insights.
Department: Management Information Systems Units 3
This course explains the fundamental trade-off in prediction: The Estimation uncertainty versus Misspecification of the Conditional Expectation. We use parsimonious models and machine learning to deal with this trade-off. The course emphasizes understanding and intuition so that you can adjust the tools to new quantitative problems that you may encounter. This distinguishes the course from an undergraduate course or an `econometric cookbook' course.
Department: Management Information Systems Units 3
This course introduces advanced statistical machine learning methods for business applications. Real-world examples are drawn from marketing, finance, and other areas for illustration.
Graduate Interdisciplinary Programs
Department: Statistics Units 3
Analysis of contingency tables. Generalized Linear Models including logistic regression and log-linear models. Matched-pair models. Repeated categorical responses. Students will be expected to utilize standard statistical software packages for computational purposes.
Department: Statistics Units 3
Analysis of contingency tables. Generalized Linear Models including logistic regression and log-linear models. Matched-pair models. Repeated categorical responses. Students will be expected to utilize standard statistical software packages for computational purposes.
James E. Rogers College of Law
Department: Law Units 2
Legal Practice in the Age of A.I. & Big Data is a two-credit experiential course where students engage with and address the ethical implications of artificial intelligence systems and AI-enabled processes in law practice scenarios.
For the bulk of the course, students will work with fellow \"associates\" to develop a technological solution that enhances the firm's pro bono efforts. Additionally, they will evaluate and utilize AI-tools to discern risks and advise on case strategy. Students will gather the skills and knowledge necessary to become critical and ethical users of expert systems and machine learning empowered technologies through these assignments.
Mel & Enid Zuckerman College of Public Health
Department: Epidemiology and Biostatistics Units 3
This course deals with the analysis of categorical data. It emphasizes applications in epidemiology, clinical trials, and other public health research, and will cover concepts and methods for binomial, multinomial, and count data, as well as proportions and incidence rates..
Department: Epidemiology and Biostatistics Units 3
This course deals with the analysis of high dimensional data. It will cover multiple comparison, clustering and classification of high dimensional data, and regression methods involving high dimensional variables. Students will also learn the corresponding computer software.
Department: Epidemiology and Biostatistics Units 3
This course will introduce students to the fundamentals of data management using the SAS programming language. Emphasis will be placed on data manipulation, including reading, processing, recoding, and reformatting data. The approach will be to teach by example, with an emphasis on hands-on learning.