2DV516 Introduction to Machine Learning

This repository contains the code and reports for the course "2DV516 Introduction to Machine Learning". The course consists of 4 assignments, each consisting of several sub-assignments. The assignments covered topics such as k Nearest Neighbors, Linear and Logistic regression, Gradient Descent, Decision trees, Ensemble methods, SVM, Neural Networks, and Unsupervised learning.

Assignments

1. k Nearest Neighbors

   • Implementing k-NN from scratch and using Scikit-learn library.
   • Exploring the effect of changing hyperparameters on the performance of the algorithm.

2. Linear and Logistic regression, Gradient Descent

   • Implementing linear and logistic regression from scratch and using Scikit-learn library.
   • Exploring the effect of regularization on the performance of the models.
   • Implementing gradient descent optimization from scratch.

3. Decision trees, Ensemble methods, SVM, and Neural Networks

   • Implementing decision trees, Random Forests, and Adaboost from scratch and using Scikit-learn library.
   • Implementing SVM using Scikit-learn library.
   • Implementing Neural Networks using TensorFlow and Scikit-learn library.
   • Exploring the effect of hyperparameters on the performance of the models.

4. Unsupervised learning

   • Implementing K-Means clustering and Principal Component Analysis from scratch and using Scikit-learn library.
   • Exploring the effect of changing hyperparameters on the performance of the algorithms.