This repository contains my practical assignments for the Machine Learning course lectured by Dr. Fatemeh Seyedsalehi on Spring 2024 semester.
The assignments include jupyter notebooks for the following topics:
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Heart_Disease_Prediction: analyzing heart disease data, training a self-defined perceptron and a naive bayes model for predicting heart disease and evaluating the performance of each model.
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MLE_MAP: Maximum Likelihood Estimation and Maximum A Postteriori estimation.
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Polynomial_Regression: polynomial regression to model the relationship between the independent variable ( X ) and the dependent variable ( y ) and fine-tuning the degree of polynomial.
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Regularization: Applying Lasso regularization (L1) and Ridge regularization (L2) on housing data and evaluating their results.
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SVM: Self-defined soft SVM model, multiclass SVM, and different SVM kernels.
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Classification: Classification with PyTorch on bank marketing dataset.
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KNN: preprocessing data and implementation of KNN model from scratch.
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kmeans: Using ELBO method and silhouette method to find optimal cluster number and comparing self-defined model with sklearn kmeans.
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dimension_reduction: Implementation of PCA model and analyzing its results on a food dataset.