Machine-Learning-1

This is the repository of the university class "Machine Learning 1"

Structure and overview

This repository contains three folders, each folder is corresponding to one project.

Each "Week ~" folder contains Jupyter Notebook, pdf file of the output, and dataset.

• Week 4
  • Plot the dataset using Histogram and Scatterplot
  • Implement my own OLS estimator
  • Compare my implementation to sklearn
  • Regularization with ridge regression
  • Cross-validation
• Week 8
  • Explore the MNIST dataset
  • Linear Discriminant Analysis to separate the classes
  • MNIST with Logistic Regression using sklearn
  • MNIST with simple Keras Fully connected neural network
  • MNIST with simple Keras CNN
• Week 10
  • Linear dimensionality reduction with PCA
  • Comparison with PCA implemented by sklearn
  • Nonlinear dimensionality reduction with t-SNE
  • Cluster the data using k-Means
  • Clustering using Gaussian Mixture Models (currently being done)