Courses and programs
Broad introduction to AI, focusing on its applications and impact across different fields and industries. Provides an overview of AI concepts, terminology, and real-world examples, allowing students to understand how AI is transforming various domains. Basic introduction of societal implications, ethical considerations, and potential challenges associated with AI. Opportunities to collaborate and use AI to address real-world challenges.
Real-world examples of ethical challenges posed by artificial intelligence (AI) technologies. How artificial intelligence affects users, the public, and society, domestically and globally. Responsibilities of AI designers, as well as public and private institutions, to those affected. Course is open to students from any major.
Basic principles, techniques, and applications of artificial intelligence. Specification, design, implementation, and applications of intelligent agents. Computational models of intelligent behavior, including problem solving, knowledge representation and reasoning, planning, decision making, learning, perception, and communication. Artificial intelligence programming. Term project and written report for graduate credit.
Risk assessment and mitigation mechanisms of various stages of machine learning/data science lifecycles with the emphasis on the early stages: study design, data acquisition, exploratory data analysis, feature engineering, modeling and training, and model evaluation. Case studies of machine learning/data science lifecycles used in application domains along with methods for risk assessment and mitigation. DS 2020 recommended.
Selected topics in probabilistic graphical models, causal inference, semantic web, information retrieval, natural language processing, knowledge representation and reasoning, deep learning, embedding, distributed learning, incremental learning, multi-task learning, multi-strategy learning, multi-relational learning, modeling the internet and the web, automated scientific discovery, neural and cognitive modeling. Advanced applications of artificial intelligence in bioinformatics, distributed systems, natural language, multimedia data, decision making, robotics, and more.
Structured query language (SQL), power BI/Tableau BI, R/Python, machine learning, and artificial intelligence (AI) to analyze exercise and health data. Data analytics applications and health systems, data mining and visualization, predictive modeling for health outcomes.
Introduction to artificial intelligence (AI) and writing. Students will learn how to find and use AI technologies such as ChatGPT to write for specific purposes, genres, and media; create effective AI prompts and search for accurate information; and navigate the ethical issues of integrating AI-generated and processed content into authored works.
This course covers advanced artificial intelligence tools and methodologies to design compelling stories about the solutions they have created in other classes/studios aimed at specific audiences to create an effective narrative. The course will utilize digital/social platforms to not only analyze the current trends but also create content.