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C S X95T C S 195T, 395T  Topics in Computer Science  1,3 Hours

1,3 Lecture Hours  0 Lab Hours  
From eight to fifteen topics are offered each semester. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester.
Pre/Corequisites: Graduate standing; additional prerequisites vary with the topic.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Doctoral  
Topic 1: Parallel Computations.

Topic 9: Introduction to Ethical AI and Robotics. Introduction to ethical issues and challenges that could and do arise when creating AI-based systems. Explore case studies anchored by readings illustrating the current and future possible uses of robots as well as the broad themes of ethical and responsible AI. Address how things have gone wrong in the past and open debates regarding how best to design, deploy, and regulate AI systems. Additional prerequisite: Graduate standing or consent of instructor.

Topic 10: Foundational Techniques of Machine Learning and Data Sciences. Introduction to computational and mathematical tools of data science. Cover statistical estimation and optimization algorithms, neural networks, geometry of high dimensional spaces, randomized methods, sparse approximation, and dimension reduction techniques. Only one of the following may be counted: Computer Science 395T (Topic 10), 395T (Topic: Fdtnl Tchnq Mchn Lrng/Data Sci), Mathematics 393C (Topic: Fdtnl Tchnq Mchn Lrng/Data Sci), Computational Science, Engineering, and Mathematics 382M, 383M.
  

C S X95T.1 C S 195T.1, 395T.1  Parallel Computations  1,3 Hours

1,3 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

C S X95T.9 C S 195T.9, 395T.9  Introduction to Ethical AI and Robotics  1,3 Hours

1,3 Lecture Hours  0 Lab Hours  
Introduction to ethical issues and challenges that could and do arise when creating AI-based systems. Explore case studies anchored by readings illustrating the current and future possible uses of robots as well as the broad themes of ethical and responsible AI. Address how things have gone wrong in the past and open debates regarding how best to design, deploy, and regulate AI systems.
Pre/Corequisites: Graduate standing or consent of instructor.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

C S X95T.10 C S 195T.10, 395T.10  Foundational Techniques of Machine Learning and Data Sciences  1,3 Hours

1,3 Lecture Hours  0 Lab Hours  
Introduction to computational and mathematical tools of data science. Cover statistical estimation and optimization algorithms, neural networks, geometry of high dimensional spaces, randomized methods, sparse approximation, and dimension reduction techniques. Only one of the following may be counted: Computer Science 395T (Topic 10), 395T (Topic: Fdtnl Tchnq Mchn Lrng/Data Sci), Mathematics 393C (Topic: Fdtnl Tchnq Mchn Lrng/Data Sci), Computational Science, Engineering, and Mathematics 382M, 383M.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral