Demystifying Machine Learning and AI Algorithms

Machine learning is rife with technical terms like "parameterization," "backpropagation," and "tokenization." Fittingly, these words seem like something only a machine would understand; technical and inhuman. But machine learning can be reduced to a handful of definitions, key concepts, and basic principles. Understanding these smaller, individual components is the key to understanding the larger systems—an approach known as "reductionism" or "reductive analysis." This non-programming course provides a comprehensive introduction to the fundamental concepts of artificial intelligence (AI) tailored for non-technical audiences. Through lectures, interactive discussions, and real-world examples, students will gain insights into the underlying mechanics of AI systems without delving deep into mathematical intricacies. The course begins by examining basic regression algorithms—models that quantify the relationship between two or more variables—and gradually builds to more complex topics, such as neural networks and deep learning algorithms that power Generative AI.Additional tools and reference materials are also provided for further exploration. By the end of this course, students will be able to meaningfully collaborate with AI practitioners, better evaluate the rapidly evolving AI landscape, and gain valuable context when assessing potential career decisions in the field.

Gaurav Khanna
Senior Manager, Data Science and Digital Journeys, Cisco Systems

Learn More

Course Page
Online, instructor-led
Feb 1 - Feb 29, 2024
Stanford Continuing Studies