DS 510 Artificial Intelligence for Data Science
This course includes the methodologies for modeling and simulating several human abilities that are widely accepted as representing intelligence. Perceiving, representing, learning, planning, and reasoning with knowledge and evidence are key themes. Concepts and methods developed for building AI systems are useful in Data Science such as knowledge graphs, computer vision algorithms, speech, and natural language processing algorithms, and machine learning algorithms. Thus, a student who is well versed in AI will be able to apply those techniques in a Data Science context. Conversely, Data Science methods are applied extensively in AI systems. Data Science students should have an understanding of AI systems and the way they work if they plan to apply their work to AI. Topics include general AI concepts, knowledge representation and reasoning (logic-based models), knowledge representation and reasoning (probability-based models), and planning and search strategies.