Undergraduate Winter 2026 Upper Level Electives Grade: #

EECS 445: Introduction to Machine Learning explores the mathematical foundations and practical implementation of supervised and unsupervised machine learning algorithms, focusing on their application to complex, real-world datasets in fields like robot perception and computer vision.

Topic 1: Statistical Learning & Predictive Modeling

This section focuses on the transition from explicit programming to data-driven inference. By implementing foundational algorithms from scratch, ranging from regularized linear models to kernel methods, I developed a rigorous pipeline for clinical risk assessment and medical data analysis.

Project #1: Clinical Risk Prediction & Kernel Methods
Python Scikit-learn RBF Kernels Regularization ($L_1/L_2$)
  • Objective: Developing a predictive classification pipeline to identify high-risk ICU patients by analyzing high-dimensional clinical time-series and static health records from the PhysioNet dataset.
  • Build: Engineered a robust preprocessing workflow including max-value feature extraction, mean imputation, and Min-Max normalization. Implemented 5-fold stratified cross-validation to optimize hyperparameters ($C$ and $\gamma$) for both Logistic Regression and Kernel Ridge Regression.
  • Functionality: Achieved high-precision mortality predictions by addressing class imbalance via asymmetric cost functions (class weighting) and evaluating performance through 1,000-sample bootstrapping to ensure statistical significance.

Topic 2:

Coming up …