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TECH 16 — Large Language Models for Business with Python

Quarter: Summer
Instructor(s): Charlie Flanagan, Dima Timofeev
Duration: 2 days
Location: On-campus
Date(s): Jul 19—Jul 20
Class Recording Available: No
Class Meeting Day: Saturday and Sunday
 
Class Meeting Time: 10:00 am—4:00 pm (PT)
Tuition: $435
   
Refund Deadline: Jul 12
 
Unit(s): 1
   
Enrollment Limit: 60
  
Status: Open
 
Quarter: Summer
Day: Saturday and Sunday
Duration: 2 days
Time: 10:00 am—4:00 pm (PT)
Date(s): Jul 19—Jul 20
Unit(s): 1
Location: On-campus
 
Tuition: $435
 
Refund Deadline: Jul 12
 
Instructor(s): Charlie Flanagan, Dima Timofeev
 
Enrollment Limit: 60
 
Recording Available: No
 
Status: Open
 
 
Large language models (LLMs) help people with the everyday aspects of their lives, including writing content, increasing personal productivity, and simplifying daily tasks. By examining OpenAI, Gemini, and other models, this transformative course offers an expansive and detailed understanding of LLMs and how they can be applied to create a competitive business advantage. The curriculum delves into the fundamental concepts, architectures, and training techniques required to create real-world applications, emphasizing hands-on experience using prominent platforms such as Python, LlamaIndex, and Hugging Face. The course also teaches students the practical skills to create large language model applications such as automatic text generators, language translators, and models that gauge consumer sentiment toward products and brands. Additionally, students will learn the following:

  • Differences between various model architectures and how to select which architecture is best suited for a particular use case
  • Techniques for efficient training and fine-tuning of models
  • Selecting and interpreting metrics that communicate how accurately the model makes predictions on new data it wasn’t trained on
The course features guest speakers from the field, interactive coding sessions, and a final project allowing students to apply their knowledge in a real-world context. By the end of the course, students will have a robust understanding of large language models and hands-on experience with various tools and libraries. They will have the skills to use these models responsibly and effectively in future work or research.

Students are expected to have a basic understanding of Python and machine learning. Prior exposure to natural language processing would be beneficial but is not required.

CHARLIE FLANAGAN
Head of Applied AI, Balyasny Asset Management

Charlie Flanagan is the head of applied AI at Balyasny Asset Management, a large multistrategy hedge fund. Earlier, he worked for Google, where he was the data science lead for Google Duplex. He received an MS in software engineering from Harvard and an MBA from Columbia.

DIMA TIMOFEEV
Research Engineer, 1X

Dima Timofeev is a research engineer at 1X, a leading company in humanoid robotics that develops general-purpose robots. He focuses on building AI infrastructure. Previously, he worked on self-driving cars at Cruise (GM's autonomous vehicle project) and Waymo (Google's self-driving car initiative). Before transitioning to embodied AI, Timofeev spent five years at Google. He received an MS in computer science and computer engineering from Peter the Great St. Petersburg Polytechnic University, Russia.

Textbooks for this course:

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