MACHINE LEARNING ALGORITHMS BY SMART IT:<p style=

ALL YOUR MACHINES MADE INTELLIGENT AND CONNECTED

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MACHINE LEARNING ALGORITHMS BY SMART IT:
ALL YOUR MACHINES MADE INTELLIGENT AND CONNECTED

M2M CHALLENGES WE CONQUER

Lack of interoperability

Vendor-specific solutions with
non-standard interfaces

Poor visibility across assets
and operations

Lack of actionable insight

Extended downtime to update and
reboot machine software

Compromised productivity

TURNING ORDINARY MACHINES INTO SOFTWARE-DEFINED MACHINES

We build custom Machine Learning algorithms for M2M
solutions to connect machines and devices to each other and to the cloud. We rapidly develop machine learning
apps that can enhance the intelligence and function of your connected assets. Machine Learning at Smart
IT involves:

A Few Words from Our Clients

“The team at Smart IT did their best to accommodate our needs and helped us with the new release of VideoNote. As part of this release, we added support for mobile experiences, allowing students to access their courses from any screen size and device. We want to take our platform to the new level, adding advanced analytics and a social interactions layer, and we look forward to working with Smart IT on this new challenge.”

Ryan Morris, Founder and President, VideoNote LLC

TYPES OF MACHINE LEARNING ALGORITHMS WE RELY ON

DEEP LEARNING

Deep Boltzman Machine (DBM)

Deep Belief Networks (DBN)

Convolutional Neural Network (CNN)

Stacked Auto-Encoders

ENSEMBLE

Random Forest

Gradient Boosting Machines (GBM)

Boosting

Bootstrapped Aggregation (Bagging)

AdaBoost

Stacked Generalization (Blending)

Gradient Boosted Regression Trees (GBRT)

NEURAL NETWORKS

Perceptron

Back-Propagation

Hopfield Network

REGULARIZATION

Least Absolute And Selection Operator (LASSO)

Elastic Net

Least Angle Regression (LARS)

RULE SYSTEM

Cubist

One Rule (OneR)

Zero Rule (ZeroR)

Repeaded Incremental Pruning to Produce Error Reduction (RIPPER)

REGRESSION

Linear Regression

Ordinary Least Squares Regression (OLSR)

Stepwise Regression

Multivariate Adaptive Regression Splines (MARS)

Locally Estimated Scatterplot Smoothing (LOESS)

Logistic Regression

BAYESIAN

Naive Bayes

Averaged One-Dependence Estimators (AODE)

Bayesian Belief Network (BBN)

Gaussian Naive Bayes

Multinomial Navie Bayes

Bayesian Network (BN)

DECISION TREE

Classification and Regression Tree (CART)

Iterative Dichtomiser 3 (ID3)

C4.5

C5.0

Chi-squared Automatic Interaction Detection (CHAID)

Decision Stump

Conditional Decisions Trees

DIMENSIONALITY

Principal Component Analysis (PCA)

Partial Least Squares Regression (PLSR)

Summon Mapping

Multidimensional Scaling (MDS)

Projection Pursuit

Principal Component Regression (PCR)

Partial Least Squares Discriminant Analysis

Mixture Discriminant Analysis (MDA)

Quadratic Discriminant Analysis (QDA)

Regularised Discriminant Analysis (RDA)

Flexible Discriminant Analysis (FDA)

Linear Discriminant Analysis (LDA)

INSTANCE BASED

k-Nearest Neighbour (kNN)

Learning Vector Quantization (LVQ)

Self-Organizing Map (SOM)

Locally Weighted Learning (LWL)

CLUSTERING

k-Means

k-Medians

Expectation Maximization

Hierarchical Clustering

LEARNING STRATEGIES

SUPERVISED LEARNING

The Machine Learning solution is exposed to the input that has a known
answer. The internal state of the solution is modified to better match the expected result.

SEMI-SUPERVISED LEARNING

The goal is to train a solution from both labeled and unlabeled samples, so
that it is better than the supervised classifier trained.

UNSUPERVISED LEARNING

The Machine Learning solution is used to draw inferences from datasets
consisting of input data without labeled responses.

WORKING WITH
ARTIFICIAL NEURAL NETWORKS

Artificial Neural Networks are typically difficult to configure and slow to
train, but once prepared are very fast in application and capable of generalization and tolerance to
noise. We recommend using them for approximation-based problem domains.

WHAT YOU’LL GET