Simple MLP implementation for a simple regression model

Machine Learning coursework from IST's Aprendizagem 2025/26. Covers decision trees, Bayesian classifiers, k-NN, linear/ridge regression, MLPs with backpropagation, k-means clustering, PCA, and LDA. Includes theoretical exercises and Python implementations using scikit-learn.

This directory contains implementation of my solution to coursework 1 of Machine Learning Practical (MLP).

This repository contains my AI coursework implementation of K-Nearest Neighbors (KNN) and Multi-Layer Perceptron (MLP)

Neural Computing Coursework. Implement and compare a Multi-Layer Perceptron (MLP) vs a Support Vector Machine (SVM)

Implementation of a Feedforward Neural Network (FFNN) from scratch using NumPy for IF3270 Machine Learning coursework. Includes forward & backward propagation, gradient descent training, regularization, hyperparameter experiments, and comparison with sklearn MLP.

From-scratch NumPy implementations of Perceptron and Multi-Layer Perceptron (MLP) for deep learning coursework, with experiments on Gaussian datasets, make_moons, and batch size analysis for BGD/SGD.

PyTorch implementations of MLP, CNN, and RNN for deep learning coursework, including comparisons on synthetic datasets, CIFAR-10 classification, and palindrome prediction tasks with training scripts and notebooks for analysis.

My Applied Deep Learning coursework portfolio exploring the Food-101 dataset. Implemented baseline MLP, custom CNN, SOM clustering on features, and autoencoder experiments with critical reflection on transfer learning vs training from scratch.