Date Lecture Readings Logistics
Module 1: Fundamentals
1/21 Lecture #1 (Prof. Lengerich):
Course Introduction & Goals
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1/26 Lecture #2 (Prof. Lengerich):
A Brief Introduction to Deep Learning
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1/28 Lecture #3 (Prof. Lengerich):
Regularization, Optimization, and Initialization in Deep Learning
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2/2 Lecture #4 (Prof. Lengerich):
Structured Models: CNNs
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2/4 Lecture #5 (Prof. Lengerich):
Skipped
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2/9 Lecture #6 (Prof. Lengerich):
Structured Models: Graphs, RNNs
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2/11 Lecture #7 (Prof. Lengerich):
A Brief Introduction to Generative Models
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2/16 Lecture #8 (Prof. Lengerich):
Attention and Transformers
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Preliminary Paper Interest and Presentation Plans Sign-up (in-class)
2/18 Lecture #9 (Prof. Lengerich):
GPTs
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2/23 Lecture #10 (Prof. Lengerich):
Unsupervised Training ("Pre-training")
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Preliminary Outline of Review Paper Topics (in-class)
2/25 Lecture #11 (Prof. Lengerich):
Supervised Fine-Tuning ("Post-training")
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Prep for papers 1 & 2
Module 2: Seminar
3/2 Lecture #12 (Prof. Lengerich):
Prompts and In-Context Learning
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3/4 Student Presentations 1 & 2
3/9 Lecture #13 (Prof. Lengerich):
Foundation Models for Clinical Images
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3/11 Student Presentations 3 & 4
3/16 Lecture #14 (Prof. Lengerich):
Foundation Models for Genomics
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3/18 Lecture #15 (Student):
Skipped
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Prep for papers 7 & 8
3/23 Student Presentations 5 & 6
3/25 Student Presentations 7 & 8
3/30 Lecture #16 :
Spring Break
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4/6 Lecture #17 (Prof. Lengerich):
Foundation Models for EHRs
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4/8 Student Presentations 9 & 10
4/13 Lecture #18 (Prof. Lengerich):
Foundation Models and Structured Data
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4/15 Student Presentations 11 & 12
4/20 Lecture #19 (Prof. Lengerich):
Review Paper & Dataset Debugging
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4/22 Student Presentations 13 & 14
4/27 Lecture #20 (Prof. Lengerich):
Open Problems & Paper Wrap-Up
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4/29 Student Presentations 15 & 16