| 1 |
1/15 |
Holiday |
1/17 |
Intro to class |
| 2 |
1/22 |
What’s different in health? modalities, imbalance, rare events, stakeholders, etc. [scribe-1][scribe-2] |
1/24 |
Intro to clinical care and understanding clinical data [scribe-1] |
| 3 |
1/29 |
Classification methods in healthcare: diagnosis, prognosis, stratification, segmentation |
1/31 |
Intro to numpy, pandas, sklearn (Lab 0) |
| 4 |
2/5 |
SVM, random forests, xgboost: interpretation of different model choices |
2/7 |
Unsupervised methods for healthcare: subtype discovery, pattern discovery (MP1 - omics) |
| 5 |
2/12 |
PCA, ICA, tensor decomposition |
2/14 |
Clustering methods (Deadline to form project groups) |
| 6 |
2/19 |
Causal Inference in Healthcare |
2/21 |
Digital health (Guest Lecture - Prof. Srivastava) (MP2 – physiological signals) |
| 7 |
2/26 |
Deep learning for healthcare: Intro |
2/28 |
ConvNets & PyTorch tutorial (Project proposals) |
| 8 |
3/4 |
Spring Break |
3/6 |
Spring Break |
| 9 |
3/11 |
CNNs for medical imaging and signals |
3/13 |
Explanations and attributions (MP3 - medical imaging) |
| 10 |
3/18 |
Machine learning for medical imaging: (Guest lecture - Google Health) |
3/20 |
Graphs and graph neural networks in healthcare |
| 11 |
3/25 |
Sequence modeling in healthcare - HMMs to RNNs – health applications |
3/27 |
Guest Lecture: ML for Mental Health (Project mid-term reports) |
| 12 |
4/1 |
Learning health systems (Guest Lecture - M Health Fairview) |
4/3 |
Unsupervised deep learning for healthcare, self-supervised learning (MP4 – clinical text) |
| 13 |
4/8 |
Generative models in healthcare |
4/10 |
AI evaluation and deployment (Guest Lecture - Institute for Health Informatics) |
| 14 |
4/15 |
Clinical foundation models |
4/17 |
Lab on large language models |
| 15 |
4/22 |
Ethical AI for healthcare (Guest Lecture - Mayo Clinic) |
4/24 |
Trustworthy Health AI - Robustness and Reliability |
| 16 |
4/29 |
Project presentations |
EOS |
Reports due |