Machine_learning

A repository for solutions of ML assignment of SUT ML course taught by Prof. Shamsollahi

Contents

1. Assignment 1

   • Part 1: Introduction to pytorch and tensors and a code for SVD for denoising images
   • Part 2: PCA for dimensionality reduction
   • Part 3: Autograd, learning process and PyTorch

2. Assignment 2

   • Part 1: Implementing the following classifiers for the breast canser dataset:

     • perceptron
     • KNN
     • SVM
     • Bayesian

   • Part 2: Implementing the Parzen window for estimating a distribution

3. Assignment 3

   • Part 1: Feature extraction and dimensionality reduction techniques like autoencoders and kernel PCA.
   • Part 2: K-means Clustering algorithm
   • Part 3: Implementing the Expectation-Maximization (EM) algorithm from scratch to train a Gaussian Mixture Model (GMM) on image datasets.
   • Part 4: Training a Random Forest model. An ensemble method is implemented that combines predictions from multiple models with max voting
   • Part 5: build a basic recommender system using the Spotify dataset. This section includes feature selection process.

4. Assignment 4

   • Part 1: Fine tunning a model
   • Part 2: A code for using decision tree for classification