Course Material for Artificial Intelligence and Machine Learning - Unit 2 @ Computer Science Dept, Sapienza
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Updated
Jun 1, 2024 - Jupyter Notebook
Course Material for Artificial Intelligence and Machine Learning - Unit 2 @ Computer Science Dept, Sapienza
Clustering high-dimensional data with Minkowski distance
Data processing utilities in keras3
K-means clustering algorithm using MapReduce.
This project implements a machine learning-based recommendation system similar to Spotify's. It predicts a user's likelihood of repeatedly listening to a song within a set timeframe
Optimal univariate k-means clustering using dynamic programming
analyze the shopping behaviors and demographic profiles of customers visiting a mall using various clustering techniques.
A curated list of Best Artificial Intelligence Resources
Data Science Content from DNC School
This project classifies images from the Flower102 dataset using k-means clustering followed by K-Nearest Neighbors (KNN) classification. It optimizes KNN parameters to achieve high accuracy, with the best results obtained using 7 clusters and 5 nearest neighbors.
Generate a color palette from an image using k-means clustering in the Oklab color space.
Dive into the world of Machine Learning in this immersive lab course, exploring open-source tools and algorithms such as random forest, SVM, linear regression, PCA, K-means, LDA, KNN, decision tree, and more. Engage in real-world ML projects and deploy your models, gaining practical experience in the forefront of AI technology.
Fast and high quality image quantization and palette generation in the sRGB, Oklab, or CIELAB color spaces.
Machine Learning | Fall 2023
clustering of electricity consumers
Scan Tailor Experimental is an interactive post-processing tool for scanned pages.
KMeans implementations in fortran
This project classifies flower images from the Oxford 102 Dataset using K-means clustering for feature extraction and machine learning for classification. It achieves efficient and accurate classification with optimized feature extraction and reduced feature sets.
Plain python implementations of basic machine learning algorithms
Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.
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