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TECH 17 — Demystifying Machine Learning and AI Algorithms

Quarter: Winter
Day(s): Thursdays
Course Format: Live Online (About Formats)
Duration: 5 weeks
Date(s): Feb 1—Feb 29
Time: 7:00—8:50 pm (PT)
Refund Deadline: Feb 3
Unit: 1
Grade Restriction: No letter grade
Tuition: $390
Instructor(s): Gaurav Khanna
Limit: 40
Class Recording Available: Yes
Status: Open
ACCESS THE SYLLABUS » (subject to change)
Live Online(About Formats)
7:00—8:50 pm (PT)
Feb 1—Feb 29
5 weeks
Refund Date
Feb 3
1 Unit
Grade Restriction
No letter grade
Gaurav Khanna
ACCESS THE SYLLABUS » (subject to change)
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 platforms, including ChatGPT.

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.

Knowledge of basic math (algebra) is recommended. No programming or computer science experience is required.

Senior Manager, Data Science and Digital Journeys, Cisco Systems

Gaurav Khanna has more than 20 years of experience in technology and entrepreneurship. During the past five years, he has led efforts to automate business workflows using machine learning and deep learning techniques. His work focuses on using large language models and generative AI to transform how users interact with sales acceleration platforms. Khanna is passionate about demystifying complex subjects and is a speaker on AI/ML topics. He received a BS in physics from Yale and an MS and a PhD in materials science and engineering from Stanford.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.