Machine Learning With F#

Grigoriy Belenkiy
Software engineer, S&P Global
@grishace

//denver/dev/day
June 24, 2016

Agenda

Machine Learning
Toolkit
Linear regression
Logistic regression
Clustering
Neural networks

Machine Learning

explores the study and construction of algorithms that can learn from and make predictions on data
focuses on prediction, based on known properties learned from the training data
spam filtering, OCR, search engines, and computer vision

Started in 50's, but abandoned and emerged back in 80's-90's
Traditionally developers, when faced with a problem, develop and algorithm and write code. Certain classes of problems, however, do not lend themselves to this approach. With machine learning, the developer instead supplies relevant data to the machine and allows the computer to create the appropriate algorithm.
Supervised learning is the branch of machine learning that deals primarily with prediction. Given examples, supervised learning algorithms create models that generalize the decision making process. In essence, the machine learns from the past in order to accurately predict the future.
Unsupervised learning is the branch of machine learning that strives to understand the structure of data. This data, unlike supervised learning, does not have a predefined outcome that requires prediction but is vast enough to require a principled approach to either visual or physical compression.

Toolkit

R (+R Tools for Visual Studio, SQL Server 2016 R Services)
MATLAB (+GNU Octave)
Python (scikit-learn, pandas, numpy, Jupyter notebook)

BIG players

Azure ML
TensorFlow
FBLearner Flow
Amazon Machine Learning(AWS)

.NET Framework

Accord.NET
Math.NET Numerics
Numl
Encog

Supervised Learning

Linear regression

Logistic regression

Linear Regression

Logistic Regression

Unsupervised Learning

K-Means

Supervised Learning (continued)

Neural Networks

XOR

	0	1
0	0	1
1	1	0

XOR

Coursera

Machine Learning https://www.coursera.org/learn/machine-learning
Введение в машинное обучение https://www.coursera.org/learn/vvedenie-mashinnoe-obuchenie

Books


Machine Learning Projects for .NET Developers by Mathias Brandewinder	Mastering .NET Machine Learning by Jamie Dixon	Machine Learning Using C# Succinctly by James McCaffrey

https://github.com/grishace/ddd-ml