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TECH 76 W — Applied AI Essentials

Quarter: Summer
Instructor(s): Mohammad Shokoohi-Yekta
Duration: 10 weeks
Format/Location: Flex Online
Date(s): Jun 24—Aug 30
Class Recording Available: Yes
Tuition: $735
Refund Deadline: Jun 27
 
Unit(s): 2
   
Enrollment Limit: 25
  
Status: Open
 
Quarter: Summer
Unit(s): 2
Duration: 10 weeks
 
Date(s): Jun 24—Aug 30
 
Format/Location: Flex Online
 
Tuition: $735
 
Refund Deadline: Jun 27
 
Instructor(s): Mohammad Shokoohi-Yekta
 
Enrollment Limit: 25
 
Recording Available: Yes
 
Status: Open
 
Artificial intelligence 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.

MOHAMMAD SHOKOOHI-YEKTA
Senior Director of AI, HeartBeam

Mohammad Shokoohi-Yekta is the author of Applications of Mining Massive Time Series Data and has been a keynote speaker at more than 65 data summits and conferences around the world. He worked at Microsoft as a lead data and applied scientist and at Apple as a data scientist. Earlier, he worked for Samsung, Bosch, GE, and UCLA Research Labs. Shokoohi-Yekta 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.