![]() ![]() ![]() In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation. For example, applications for hand-writing recognition use classification to recognize letters and numbers. Use classification if your data can be tagged, categorized, or separated into specific groups or classes. Typical applications include medical imaging, speech recognition, and credit scoring. Classification models classify input data into categories. Supervised learning uses classification and regression techniques to develop machine learning models.Ĭlassification techniques predict discrete responses-for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Use supervised learning if you have known data for the output you are trying to predict. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. In order to run the demo of your choice you should move to the chosen folder (i.e.Supervised machine learning builds a model that makes predictions based on evidence in the presence of uncertainty. In case of MatLab you may also use its web-version. Thus in order to launch demos you need either Octave or MatLab to be installed on you local machine. This repository contains *.m scripts that are intended to be run in Octave or MatLab. The source of the following machine learning topics map is this wonderful blog post How to Use This Repository Install Octave or MatLab □ Neural Network: Multilayer Perceptron (MLP) - example: handwritten digits recognition. Usage examples: as a substitute of all other algorithms in general, image recognition, voice recognition, image processing (applying specific style), language translation, etc. The neural network itself isn't an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. □ Anomaly Detection using Gaussian distribution - example: detect overloaded server. Usage examples: intrusion detection, fraud detection, system health monitoring, removing anomalous data from the dataset etc. Anomaly DetectionĪnomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. □ K-means algorithm - example: split data into three clusters. Usage examples: market segmentation, social networks analysis, organize computing clusters, astronomical data analysis, image compression, etc. The algorithm itself decides what characteristic to use for splitting. In clustering problems we split the training examples by unknown characteristics. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. □ Logistic Regression - examples: microchip fitness detection, handwritten digits recognitions using one-vs-all approach. ![]() Usage examples: spam-filters, language detection, finding similar documents, handwritten letters recognition, etc. In classification problems we split input examples by certain characteristic. □ Linear Regression - example: house prices prediction. Usage examples: stock price forecast, sales analysis, dependency of any number, etc. Basically we try to draw a line/plane/n-dimensional plane along the training examples. In regression problems we do real value predictions. The ultimate purpose is to find such model parameters that will successfully continue correct input→output mapping (predictions) even for new input examples. Then we're training our model (machine learning algorithm parameters) to map the input to the output correctly (to do correct prediction). In supervised learning we have a set of training data as an input and a set of labels or "correct answers" for each training set as an output. In most cases the explanations are based on this great machine learning course. ![]() The purpose of this repository was not to implement machine learning algorithms using 3 rd party libraries or Octave/MatLab "one-liners" but rather to practice and to better understand the mathematics behind each algorithm. This repository contains MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics behind them being explained. For Python/Jupyter version of this repository please check homemade-machine-learning project. ![]()
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