MIT

6.7930 / HST.956: Machine Learning for Healthcare

Spring 2025 · Teaching Assistant · with Prof. David Sontag & Prof. Peter Szolovits

Graduate-level course covering machine learning methods applied to healthcare, including clinical NLP, causal inference, survival analysis, interpretability, fairness, and more. Lecture slides and course materials are available on the course website.

Below are the recitation slides I prepared:

Recitation 2: Bayes' Theorem, Differential Diagnosis, Evaluation Metrics

Recitation 2: Bayes' Theorem, Differential Diagnosis, Evaluation Metrics

6.7930 / HST.956, Spring 2025

Slides
Recitation 5: Missing Data, Survival Analysis

Recitation 5: Missing Data, Survival Analysis

6.7930 / HST.956, Spring 2025

Slides
Recitation 6: Causal Inference

Recitation 6: Causal Inference

Ignorability, Sensitivity Analysis, Negative Controls, Overlap, Extrapolation

6.7930 / HST.956, Spring 2025

Slides
Recitation 8: Interpretability of ML Models

Recitation 8: Interpretability of ML Models

Linear Models, LIME, Influence Functions, Mechanistic Interpretability

6.7930 / HST.956, Spring 2025

Slides

Bilkent University

EEE 485/585: Statistical Learning and Data Analytics

Spring 2022 · Teaching Assistant

EEE 212: Microcontrollers and Embedded Systems

Fall 2020/21, Spring 2021 · Teaching Assistant