There are 3 repositories under statistical-machine-learning topic.
[ICLR 2022] Graph-Relational Domain Adaptation
ICML 2018: "Adversarial Time-to-Event Modeling"
PyTorch implementation of 'Concrete Dropout'
Compare Naive Bayes, SVM, XGBoost, Bagging, AdaBoost, K-Nearest Neighbors, Random Forests for classification of Malaria Cells
Tutorials for MATH 4432 Statistical Machine Learning, HKUST, Fall 2022
This repository contains assignments solutions for one of my postgraduate subjects of COMP SCI 7314 - Introduction to Statistical Machine Learning. The programming language is Python.
Clean your Text for Statistical ML and Language Model
CSE 575 Statistical Machine Learning
Statistical Machine Learning is a 3xx-level course offered to undergrads at IIIT-Delhi.
Code for the paper: LogGENE, A Smooth alternative for the Check Loss
Attempting to make Statistics for Machine Learning easy to learn and understand
Python implementation of Bayesian Online Changepoint Detection, Adams, R. P., & MacKay, D. J. C. (2007)
machine learning is a combination of statistics,computer science and mathematics. It uses a lot of statistical tool and mainly a lot of interpretation terms are adapt from statistics .So here I will add little but important concepts of statistics in ML.
Conversational Lexical Affect Recognition Kit
A comprehensive machine learning analysis of hotel data scraped from Booking.com in Ho Chi Minh City, Vietnam. This project leverages various ML techniques to extract insights from hotel reviews, images, and metadata to provide actionable intelligence for the hospitality industry.
Loss J's statistical machine learning course. 🚀
Analyzing the binary gender difference in lead roles using statistical machine learning
Projeto para explorar dados de vendas, identificar padrões e criar modelos preditivos que otimizem estratégias comerciais e melhorem o relacionamento com clientes.
IEEE TNNLS 2020: "Calibration and Uncertainty in Neural Time-to-Event Modeling"
Recognizing the emotion of an speech audio with ConvNet, SVM, KNN and Logistic Regression
Assignments of lecture
Development and Evaluation of Neural Net Sensitivity-Based Pruning Algorithms Using Statistical Inference
This project aim to build a recommendation system for Steam games. It utilizes different machine learning models including LSTM, ResNet, and NCF (Neural Collaborative Filtering) to predict and recommend games based on user data and interactions.
Statistical Machine Learning coursework (Sharif University of technology)
Developed and deployed a predictive model for estimating housing prices using the Ames Housing Dataset. Utilized advanced regression techniques and feature engineering to achieve high accuracy. Successfully implemented the model as an interactive web application on Streamlit, enabling real-time predictions.
Solutions from Elements of Statistical Machine Learning course at AGH UST. If you use it and it helped you please leave a star ⭐
A supervised machine learning project where I develop a number of models to classify an individual's income level using census data.
Grimoire Guide is a book recommendation system which leverages statistical machine learning and NLP concepts to recommend the best books to the users. Just provide a plot or description, and it will do the rest of the job for you.
My assignments/project for MATH5470 Statistical Machine Learning
Project in the course TDDE16 - Text Mining at Linköping University
The goal of this project is to implement a graph label propagation method to study and classify image data. The method has the particularity that it combines well with other classifiers to achieve enhanced performance.
The Muffin vs Chihuahua NN assignment for the Statistical Methods for ML course of UNIMi.
Arizona State University - EEE 549 Statistical Machine Learning Fall 2024
The Flight Price Prediction project uses machine learning to forecast flight ticket prices based on historical data. Hosted on Streamlit Community Cloud and deployed via Streamlit, this application allows users to input flight details such as departure and arrival airports, travel dates, and class to receive accurate price predictions.
Authorship attribution is a task to identify the author of a given document. Our task is to come up with test predictions for an authorship attribution problem given a training set and test inputs.