GENERATIVE ARTIFICIAL INTELLIGENCE FOR ENGINEERING APPLICATIONS

PHILOS 244

Introduction to contemporary moral and political issues in Artificial Intelligence (AI) and Data Ethics, integrating urgent problems, controversies, and continuously updated case studies. Introduces basic technical concepts such as the bias/variance tradeoff, the reference class problem, and inductive risk. Covers topics such as data and privacy, the impacts of automation on society, and the use of algorithms in medicine and criminal law.

COMPSCI 769

Develop algorithms and mathematical models for natural language processing tasks, including text categorization, information retrieval, speech recognition, machine translation, and information extraction. Focus is on the state-of-the-art computational techniques as they are applied to natural language tasks. Students are strongly encouraged to have knowledge of introductory artificial intelligence (e.g.,COMP SCI 540).

COMARTS 343

Explore the rapidly evolving landscape of communication technologies, with a particular focus on generative artificial intelligence (AI), and discuss its role in communication contexts. Examine the transformative effects of these emerging technologies on individual, social, and professional lives. Consider the potential dark sides of AI, including ethical concerns, biases, and the implications of over-reliance on technology in communication.

COMPSCI/ECE 763

Explore security and privacy aspects of trustworthy artificial intelligence. Three core subjects will be considered: differential privacy and algorithmic fairness; adversarial machine learning; and end-to-end trustworthy systems. A selection of more advanced topics may be covered such as additional notions of privacy, language-based security, and robust optimization. Knowledge of probability/statistics (such as MATH 431), cryptography (such as MATH 435), security (such asCOMP SCI 642), and modern machine learning (such asM E/​COMP SCI/​E C E  539or540) is required.

COMPSCI 540

Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.