TECH 16 — Large Language Models for Business with Python
Quarter: Winter
Instructor(s): Charlie Flanagan, Dima Timofeev
Date(s): Jan 31—Feb 1
Class Recording Available: No
Class Meeting Day: Saturday and Sunday
Grade Restriction: No letter grade
Class Meeting Time: 10:00 am—4:00 pm (PT)
Tuition: $445
Refund Deadline: Jan 24
Unit(s): 1
Enrollment Limit: 60
Status: Registration opens Dec 1, 8:30 am (PT)
Quarter: Winter
Day: Saturday and Sunday
Duration: 2 days
Time: 10:00 am—4:00 pm (PT)
Date(s): Jan 31—Feb 1
Unit(s): 1
Tuition: $445
Refund Deadline: Jan 24
Instructor(s): Charlie Flanagan, Dima Timofeev
Grade Restriction: No letter grade
Enrollment Limit: 60
Recording Available: No
Status: Registration opens Dec 1, 8:30 am (PT)
Large language models (LLMs) are revolutionizing how people write content, enhance productivity, and simplify everyday tasks. By examining OpenAI, Gemini, and other models, this course offers an understanding of LLMs and how they can be applied to create a competitive business advantage. The curriculum delves into fundamental concepts, architectures, and training techniques to build real-world applications, emphasizing hands-on experience using platforms such as Python, LlamaIndex, and Hugging Face.
Students will learn practical skills to create LLM applications such as automatic text generators, language translators, and models that gauge consumer sentiment, along with:
- Differences between model architectures and selecting which is best suited for use
- Techniques for efficient training and fine-tuning
- Selecting and interpreting metrics that communicate how accurately models make predictions
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 data science 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, Balyasny Asset Management
Dima Timofeev is an experienced engineer with over a decade in the industry, specializing in software engineering, AI/ML, autonomous systems, distributed systems, and large-scale data processing. He focuses on building AI infrastructure. Previously, he worked as a research engineer at 1X and 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, Russia.
Textbooks for this course:
There are no required textbooks; however, some fee-based online readings may be assigned.