Artificial Intelligence (A I)
A I 380E Ethics in Artificial Intelligence 3 Hours
3 Lecture Hours 0 Lab Hours
Examine important responsibilities and ethical challenges faced by an AI professional. Identify design decisions with ethical implications and consider the perspectives of users and other stakeholders when making ethically significant design decisions. Artificial Intelligence 395T (Topic: Ethics in AI) and Artificial Intelligence 380E may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 380H Artificial Intelligence in Healthcare 3 Hours
3 Lecture Hours 0 Lab Hours
Explore health IT systems, ranging from data semantics, data interoperability, diagnosis code, to workflow in clinical decision support systems. Discuss how AI innovations are transforming healthcare systems by focusing on AI in drug discovery, AI in medical image diagnosis, explainable AI for health risk prediction, and ethics of AI in healthcare. Artificial Intelligence 395T (Topic: AI in Healthcare) and 380H may not both be counted. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 388 Natural Language Processing 3 Hours
3 Lecture Hours 0 Lab Hours
Explore computational methods for syntactic and semantic analysis of structures representing meanings of natural language; study of current natural language processing systems; methods for computing outlines and discourse structures of descriptive text. Only one of the following may be counted: Artificial Intelligence 388, Computer Science 388, Data Science 388, 395T (Topic: Natural Language Processing) Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing, and a course in artificial intelligence or consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 388J Optimization 3 Hours
3 Lecture Hours 0 Lab Hours
Explore the background on convex sets and functions, linear programming, convex programming, and iterative first-order and second-order methods. Only one of the following may be counted: Artificial Intelligence 388J, Computer Science 388J, 395T (Topic: Optimization). Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 388K Online Learning and Optimization 3 Hours
3 Lecture Hours 0 Lab Hours
Explore algorithms for convex optimization and algorithms for online learning. Focus on algorithms for large scale convex optimization. Discuss problems in machine learning. Apply these ideas to online learning. Only one of the following may be counted: Artificial Intelligence 388K, Computer Science 388K, 395T (Topic: Online Lrng/Optimization). Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 388U Planning, Search, and Reasoning Under Uncertainty 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to three key foundational problems in AI: planning, search, and reasoning under uncertainty. Investigate how to define planning domains, including representations for world states and actions, covering both symbolic and path planning. Study algorithms to efficiently find valid plans with or without optimality, and partially ordered, or fully specified solutions. Only one of the following may be counted: Artificial Intelligence 388U, Computer Science 388U, 395T (Topic: Planning Search and Reasoning). Three lecture hours a week for one semester
Pre/Corequisites: Graduate standing; and knowledge of undergraduate data structures, algorithms, and complexity; and prior knowledge in probability theory or statistics.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 389L Automated Logical Reasoning 3 Hours
3 Lecture Hours 0 Lab Hours
Explore automated reasoning techniques for propositional logic, first-order logic, linear arithmetic over reals and integers, theory of uninterpreted functions, and combinations of these theories. Examine automated logical reasoning both from a theoretical and practical perspective, including building useful tools, such as SAT and SMT solvers. Only one of the following may be counted: Computer Science 389L, 395T (Topic: Automated Logical Reasoning), Artificial Intelligence 389L. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 391L Machine Learning 3 Hours
3 Lecture Hours 0 Lab Hours
Explore computing systems that automatically improve their performance with experience, including various approaches to inductive classification such as version space, decision tree, rule-based, neural network, Bayesian, and instance-based methods; as well as computational learning theory, explanation-based learning, and knowledge refinement. Only one of the following may be counted: Artificial Intelligence 391L, Computer Science 391L, Data Science 391L. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing, and Computer Science 381K or equivalent knowledge of artificial intelligence and LISP.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 391M Case Studies in Machine Learning 3 Hours
3 Lecture Hours 0 Lab Hours
Explore major concepts, techniques, algorithms, and applications in machine learning. Evaluate machine learning methods and discuss practical case studies to produce solutions for real-world data analysis problems. Only one of the following may be counted: Artificial Intelligence 395T (Topic: Case Studies/Machine Learn), 391M, Computer Science 395T (Topic: Case Studies/Machine Learn), 391M. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 394D Deep Learning 3 Hours
3 Lecture Hours 0 Lab Hours
Explore the basic building blocks and intuitions behind designing, training, tuning, and monitoring of deep networks. Examine both the theory of deep learning, as well as hands-on implementation sessions in pytorch. Explore a series of application areas of deep networks in: computer vision, sequence modeling in natural language processing, deep reinforcement learning, generative modeling, and adversarial learning. Only one of the following may be counted: Artificial Intelligence 394D, Computer Science 394D, Data Science 394D, 395T (Topic: Deep Learning). Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing, and experience in artificial intelligence and machine learning.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 394R Reinforcement Learning: Theory and Practice 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to the theory and practice of modern reinforcement learning, with emphasis on temporal difference learning algorithms. Only one of the following may be counted: Artificial Intelligence 394R, Computer Science 394R, Data Science 394R, 395T (Topic: Reinforcement Learning) Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: No
Academic Level: Master's
A I 395T Topics in Artificial Intelligence 3 Hours
3 Lecture Hours 0 Lab Hours
Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Master's
A I 698 Thesis 6 Hours
3 Lecture Hours 0 Lab Hours
Artificial Intelligence 698A and 395T (Topic: Thesis A) may not both be counted. Artificial Intelligence 698B and 395T (Topic: Thesis B) may not both be counted. The equivalent of three lecture hours a week for two semesters.
Pre/Corequisites: For 698A, graduate standing in Artificial Intelligence and consent of the graduate advisor; for 698B, Artificial Intelligence 698A.
Grading: Credit No Credit
Repeatable for credit: No
Academic Level: Master's