Matthew Quick

3 Seguidores

Dynamic, flexible data scientist. Expert on measuring what is going on to provide executive management with the empirical information needed to make real-time decisions. Analysis providing a data-driven approach for product owners and managers. Specific acumen includes statistical analysis methods, comprising GLM, Bayesian inference, vector auto-regression (VAR), NLDR, machine learning, and predictive modeling - essentially any tool to complete the job. Awarded interdepartmental recognition for delivering resilient recommendation platforms, systems, and network designs in a complete and accessible format. Consistently promoted to positions of increased responsibility.

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Matthew Quick

Intro RINA

An Introduction to Supervised Machine Learning and Pattern Classification: The Big Picture

Bayes learning

Incremental Discretization for Naive Bayes Learning using FIFFD

Risk Classification with an Adaptive Naive Bayes Kernel Machine Model

Bayesian Machine Learning & Python – Naïve Bayes (PyData SV 2013)

2013-1 Machine Learning Lecture 03 - Naïve Bayes Classifiers

Bayesian Machine Learning - Naive Bayes

Machine Learning and Statistical Analysis

Neural networks, naïve bayes and decision tree machine learning

Demand Estimation

Introduction to Statistical Machine Learning

Machine learning with scikitlearn

Kalman Filter | Statistics

Transformation of variables

Risk Model Methodologies

Support Vector Machines

Machine learning algorithms and business use cases

Statistical Models for Massive Web Data

A statistical model for undecidable viral detection