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TECH 76 W — Applied AI Essentials

Quarter: Spring
Course Format: Flex Online (About Formats)
Duration: 10 weeks
Date(s): Apr 1—Jun 7
Refund Deadline: Apr 4
Units: 2
Tuition: $735
Instructor(s): Mohammad Shokoohi-Yekta
Limit: 25
Class Recording Available: Yes
Status: Open
ACCESS THE SYLLABUS » (subject to change)
Flex Online(About Formats)
Apr 1—Jun 7
10 weeks
Refund Date
Apr 4
2 Units
Mohammad Shokoohi-Yekta
ACCESS THE SYLLABUS » (subject to change)
AI isn't as complicated as it might seem. By referencing everyday applications such as targeted marketing, web analysis, disease diagnosis, weather forecasting, and fraud detection, we can build upon pre-existing mental models to understand the underlying mechanics of AI. This course will present a high-level overview of three main topics in AI: introductory machine learning concepts, basic analysis and visualization of data, and basic programming in R (a programming language commonly used in data science and well suited for those with little to no programming experience). The course will include lectures and hands-on, interactive problem-solving exercises to help students connect conceptual challenges to tangible data solutions. Examples will be based on real-world problems in weather, marketing, biology, stocks, neuroscience, medicine, and generative AI. By the end of the course, students will be able to apply basic AI techniques to practical applications to draw meaningful conclusions.

No programming or computer science experience is required.

Senior Director of AI, HeartBeam

Mohammad Shokoohi-Yekta has been a keynote speaker at more than 65 data and AI summits around the world. Before HeartBeam, he was the lead AI scientist at Microsoft and a data scientist at Apple, and he has worked for Samsung, Bosch, GE, and UCLA Research Labs. He is the author of Applications of Mining Massive Time Series Data. He received a PhD in computer science from UC Riverside.

Textbooks for this course:

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