This repository contains the homework and their corresponding reports for the course EE 399 (Machine Learning for Science and Engineering) at the University of Washington.
Course Date: Spring 2023
Professor: Dr. Nathan Kutz
- Homework 1: Experimenting with Curve Fitting
- Homework 2: Exploring Correlation and Dimensionality Reduction Techniques on Yalefaces Dataset
- Homework 3: Analysis and Classification of the MNIST Dataset using SVD, LDA, SVM, and Decision Trees
- Homework 4: Neural Network Analysis and Model Comparison on Interpolation and Extrapolation Tasks and MNIST Data Set
- Homework 5: Predicting Lorenz System Behavior with Feed Forward, Long Short Term Memory, Recurrent, and Echo State Networks
- Homework 6: Analyzing the Performance of a SHRED Model on Sea-Surface Temperature Data with Respect to Time Lag, Noise, and Number of Sensors