This course provides an introduction to generative artificial intelligence. This course will provide students with an understanding of how to formulate generative problems, utilize generative artificial intelligence tools to create solutions, and evaluate said solutions. This course will also introduce and discuss the ethical concerns associated with generative artificial intelligence (fairness, bias, trust, explainability). This course will emphasize using generative tools (hyperparameter tuning, relationship of inputs to outputs, etc.) over programming. Topics include: problem formulation (definition and evaluation), rule-based approaches (generative grammars), Markov chains, recurrent approaches (RNNs, LSTMs, and GRUs), advanced architectures (GANs, Seq2Seq, and transformers), and applications of generative artificial intelligence.

This course provides an introduction to generative artificial intelligence. This course will provide students with an understanding of how to formulate generative problems, utilize generative artificial intelligence tools to create solutions, and evaluate said solutions. This course will also introduce and discuss the ethical concerns associated with generative artificial intelligence (fairness, bias, trust, explainability). This course will emphasize using generative tools (hyperparameter tuning, relationship of inputs to outputs, etc.) over programming. Topics include: problem formulation (definition and evaluation), rule-based approaches (generative grammars), Markov chains, recurrent approaches (RNNs, LSTMs, and GRUs), advanced architectures (GANs, Seq2Seq, and transformers), and applications of generative artificial intelligence.