Artificial Intelligence

Apply foundational knowledge of data structures, algorithms, and statistical methods to design, implement, and analyze AI systems. These foundations are critical for applying AI methods in applications.

Demonstrate understanding of core AI techniques, including machine learning, deep learning, natural language processing (NLP), computer vision, and robotics, as well as the ability to adapt to new methods as the field evolves. Given the rapid pace of AI innovation, students must graduate with both breadth and adaptability.

Work effectively with large-scale data and computational resources. This includes understanding techniques for data preprocessing, feature engineering, distributed training, and deployment in cloud environments. These skills are essential for building real-world AI systems that operate at scale.

Formulate new computational tasks in terms of established AI problem classes. To apply AI to novel domains, it is crucial to develop an ability to apply the appropriate AI techniques (e.g., search, constraint satisfaction, probabilistic inference, deep learning, etc.) to a new problem of interest.

Evaluate and interpret AI system outputs. Understanding model capabilities, limitations, interpretability, and performance trade-offs are critical for modern AI systems. This competency ensures graduates can critically assess AI models rather than treating them as black boxes, a key requirement for using AI in broader, potentially sensitive, applications.