  In this thesis the aim is to work on optimizing the modern machine learning
models for personalized forecasting of Alzheimer Disease (AD) Progression from
clinical trial data. The data comes from the TADPOLE challenge, which is one of
the largest publicly available datasets for AD research (ADNI dataset). The
goal of the project is to develop machine learning models that can be used to
perform personalized forecasts of the participants cognitive changes (e.g.,
ADAS-Cog13 scores) over the time period of 6,12, 18 and 24 months in the future
and the change in Clinical Status (CS) i.e., whether a person will convert to
AD within 2 years or not. This is important for informing current clinical
trials and better design of future clinical trials for AD. We will work with
personalized Gaussian processes as machine learning models to predict
ADAS-Cog13 score and Cox model along with a classifier to predict the
conversion in a patient within 2 years.This project is done with the
collaboration with researchers from the MIT MediaLab.
