In this project, I explore various machine learning techniques including Principal Component Analysis (PCA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Sentiment Analysis in an effort to predict the directional changes in exchange rates for a list of developed and developing countries.

M.Sc. Thesis on "Predicting Mutation Scores using Source Code and Test Suite Metrics"

Term project for Image Analysis, CS484, in Fall 2016 at Bilkent University

Using machine learning to analyse the ravelry yarn dataset

This is the prototype for my bachelor thesis for activity recognition

A simple SVM talk (COW 2013, Dolní Lomná).

Cardiac SPECT diagnosis using Support Vector Machines (SVM) and Neural Networks

Official document that explains the implementation of the automatic annotator to classify voice segments according the intonation system of Cuba.

Our study focused on using the Big Five personality inventory to predict traits from students' smartphone sensor data collected over 2 months under the Horizon Europe project. Through correlation analyses and machine learning with cross-validation, we showed that predictions are reliable and accurate enough for practical use.

机器学习支持向量机算法综述(课设作业)

Machine Learning Talk: Support Vector Machines

Positive and Negative Sentiments Analysis on Twitter Data

Lecture Notes on Advanced Machine Learning (AML)

Repository for the assignments of the Real-Time Learning in Intelligent Systems Course (Doctoral Program in Information Science and Technology)

Second assignment for Machine Learning course @USI19/20.