David Mimno is an Associate Professor and Chair of the Department of Information Science in the Ann S. Bowers College of Computing and Information Science at Cornell University. He holds a Ph.D. from UMass Amherst and was previously the head programmer at the Perseus Project at Tufts as well as a researcher at Princeton University. Professor Mimno’s work has been supported by the Sloan Foundation, the NEH, and the NSF.
Large Language Model FundamentalsCornell Certificate Program
Large Language Model Fundamentals (CISC10)
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Overview
How It Works
Format
All Online
Time Commitment
3 months with 6 to 8 hours of study per week
Learn From Top Minds
Courses are developed by Cornell faculty.
Power Your Career
Gain today’s most in-demand skills to stand apart.
Flexibility Fits Your Life
Learn on your schedule without stepping out of your job.
Small-class Experience
Participate in facilitated discussions and live sessions with industry peers.
Real-world Projects
Apply learnings and insights to your work to make an impact right away.
Personalized Feedback
Enjoy meaningful feedback on assignments from expert facilitators.
Format
All Online
Time Commitment
3 months with 6 to 8 hours of study per week
Learn From Top Minds
Courses are developed by Cornell faculty.
Power Your Career
Gain today’s most in-demand skills to stand apart.
Flexibility Fits Your Life
Learn on your schedule without stepping out of your job.
Small-class Experience
Participate in facilitated discussions and live sessions with industry peers.
Real-world Projects
Apply learnings and insights to your work to make an impact right away.
Personalized Feedback
Enjoy meaningful feedback on assignments from expert facilitators.
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Faculty Author
David Mimno
Associate Professor
Cornell Bowers Computing and Information Science
Associate Professor and Chair of the Department of Information Science, Cornell Bowers Computing and Information Science
Key Course Takeaways
- Set up and utilize large language models for generating accurate text responses
- Apply probability concepts to interpret and compare model predictions
- Customize language models for specific tasks using guided instructions
- Explore the influence of text datasets and tokenization on model output
- Analyze how training data sources and preparation strategies shape model capabilities
- Examine advanced model structures like attention layers and word embeddings to understand language generation

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