Andrew Ferlitsch

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Research Scientist with over 20 years experience in research fields and 30 years of industry experience. Have worked a wide range of technology fields for commercial driven research. Primary expertise in last 5+ years has been on geospatial technologies, data science/analytics, open/big data, and autonomous vehicles. Secondary expertise in cloud deployment, databases, digital signage, teleconferencing software, sensor grids, energy management. Long history of contributing back to the field from shareware (80s) to open source (90s) to crowd-sourcing and micro-incubating (2000s). Established and well-recognized in my field among peers international. Decades of experienced working in multi...

ai machine learning data science artificial intelligence neural networks artifical intelligence python statistics robotics datasets natural language processing search argmax central limit theorem squashing function z-score reinforcement learning perceptron softmax bellman equations markov principles conditional probability probability local search greedy a-star graph search ids dfs bfs intelligent agent learning agent pareto testing quaity assurance qa whiteboard interview coding challenges software programming object oriented programming oop nlp machine learniing recurrent neural networks convolutional neural networks pandas numpy random forest ensemble methods linear regression supervised learning training data dataset tensorflow dynamic programming graph

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About

Andrew Ferlitsch

Presentations

Graph Theory

AI Greedy and A-STAR Search

AI - Local Search - Hill Climbing

AI - Monty Hall Paradox

AI - Introduction to Markov Principles

AI - Introduction to Bellman Equations

Machine Learning - Neural Networks - Perceptron

AI - Introduction to Reinforcement Learning

Statistics - Basics

Statistics - ArgMax Equation

Statistics - SoftMax Equation

AI - Introduction to Dynamic Programming

Introduction to Machine Learning

Machine Learning - Introduction to Tensorflow

Machine Learning - Dataset Preparation

Machine Learning - Splitting Datasets

Machine Learning - Dummy Variable Conversion

ML - Simple Linear Regression

ML - Multiple Linear Regression

Machine Learning - Ensemble Methods

Machine Learning - Accuracy and Confusion Matrix

Python - Numpy/Pandas/Matplot Machine Learning Libraries

Machine Learning - Introduction to Neural Networks

Machine Learning - Introduction to Convolutional Neural Networks

Machine Learning - Introduction to Recurrent Neural Networks

Natural Language Provessing - Handling Narrarive Fields in Datasets for Classification

Natural Language Processing - Groupings (Associations) Generation

Python - Installing and Using Python and Jupyter Notepad

Python - OOP Programming

Object Oriented Programming Principles