Statistical pattern recognition course projects from shiraz university.
Reference implementation of the Distance-Based Boolean Applicability Domain for HTS datasets
Containing various ML projects, including image and text classification, clustering, regression, and neural networks. Projects include implementations of Random Forest, SVM, Decision Trees, EM algorithms, and advanced models like MLP and CNN, with datasets ranging from facial emotion detection to ECG signals and more.
Implementation of Kernel Density Estimation for Naive Bayes from scratch
The 1st project uses daily stock data from AlphaVantage API to predict the next day's TSLA closing price based on past values using Linear and Polynomial Regression with Regularization variances. The 2nd project processes Wavelet Transform features from banknotes and performs binary classification using Parzen windows, k-NN, and variations of SVMs.
Implementation of a sketch‐recognition pipeline inspired by Google’s Quick, Draw!. Includes data preprocessing and feature‐engineering scripts, three Bayesian classifiers alongside Logistic Regression, SVM, K-NN and XGBoost baselines, and an RNN model.
Classification of wine quality using a hard_parzen and a soft_parzen with gaussian kernel models - Machine Learning course (IFT3395)
Project to implement Gaussian mixture model and Parzen window estimation.
Performing non-parametric Parzen Window density estimation with a Gaussian kernel, implementing the method both from scratch and using built-in functions, and compares the results across various window widths and sample sizes.
Sketchify-A-Quick-Draw is a drawing classifier that uses machine learning to recognize hand-drawn sketches. It helps users identify and categorize their artwork quickly, making it a useful tool for artists and hobbyists alike.
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