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Machine learning is the subfield ofcomputerscience that gives computers the ability to learn withoutbeingexplicitly programmed. Evolved from the study ofpatternrecognition and computational learning theory inartificialintelligence,machine learning explores the study andconstructionof algorithms that can learn from and make predictionson data –such algorithms overcome following strictly staticprograminstructions by making data driven predictions ordecisions,through building a model from sample inputs. Machinelearning isemployed in a range of computing tasks where designingandprogramming explicit algorithms is infeasible; exampleapplicationsinclude spam filtering, detection of network intrudersor maliciousinsiders working towards a data breach, opticalcharacterrecognition search engines and computer vision.Machine learning is closely related to (and often overlapswith)computational statistics, which also focuses inprediction-makingthrough the use of computers. It has strong tiesto mathematicaloptimization, which delivers methods, theory andapplicationdomains to the field. Machine learning is sometimesconflated withdata miningwhere the latter subfield focuses more onexploratorydata analysis and is known as unsupervised learning.Machinelearning can also be unsupervised and be used to learnandestablish baseline behavioral profiles for various entitiesandthen used to find meaningful anomalies.Within the field of data analytics, machine learning is amethodused to devise complex models and algorithms that lendthemselvesto prediction; in commercial use, this is known aspredictiveanalytics. These analytical models allow researchers,datascientists, engineers, and analysts to "producereliable,repeatable decisions and results" and uncover "hiddeninsights"through learning from historical relationships and trendsin thedata.- Decision tree learning- Association rule learning- Artificial neural networks- Deep learning- Inductive logic programming- Support vector machines- Clustering- Bayesian networks- Reinforcement learning- Representation learning- Similarity and metric learning- Sparse dictionary learning- Genetic algorithms- Rule-based machine learning- Learning classifier systems

Deep learning (also known as deepstructuredlearning, hierarchical learning or deep machine learning)is aclass of machine learning algorithms thatuse a cascade of many layers of nonlinear processing unitsforfeature extraction and transformation. Each successive layerusesthe output from the previous layer as input. The algorithms maybesupervised or unsupervised and applications includepatternanalysis (unsupervised) and classification(supervised).are based on the (unsupervised) learning of multiple levelsoffeatures or representations of the data. Higher level featuresarederived from lower level features to form ahierarchicalrepresentation.are part of the broader machine learning field oflearningrepresentations of data.learn multiple levels of representations that correspondtodifferent levels of abstraction; the levels form a hierarchyofconcepts.Deep learning based on a set of algorithms that attempt tomodelhigh level abstractions in data. In a simple case, there mightbetwo sets of neurons: one set that receives an input signal andonethat sends an output signal. When the input layer receives aninputit passes on a modified version of the input to the nextlayer. Ina deep network, there are many layers between the inputand theoutput (and the layers are not made of neurons but it canhelp tothink of it that way), allowing the algorithm to usemultipleprocessing layers, composed of multiple linear andnon-lineartransformations.ApplicationsAutomatic speech recognitionImage recognitionNatural language processingDrug discovery and toxicologyCustomer relationship managementRecommendation systemsBiomedical informatics

Statistics is a branch of mathematicsdealingwith the collection, analysis, interpretation, presentation,andorganization of data. In applying statistics to, e.g.,ascientific, industrial, or social problem, it is conventionaltobegin with a statistical population or a statistical modelprocessto be studied. Populations can be diverse topics such as"allpeople living in a country" or "every atom composing acrystal".Statistics deals with all aspects of data including theplanning ofdata collection in terms of the design of surveysandexperiments.Some popular definitions are:Merriam-Webster dictionary defines statistics as "a branchofmathematics dealing with the collection, analysis,interpretation,and presentation of masses of numerical data.Statistician Sir Arthur Lyon Bowley defines statistics as"Numericalstatements of facts in any department of inquiry placedin relationto each other".When census data cannot be collected, statisticians collect databydeveloping specific experiment designs and surveysamples.Representative sampling assures that inferences andconclusions cansafely extend from the sample to the population as awhole. Anexperimental study involves taking measurements of thesystem understudy, manipulating the system, and then takingadditionalmeasurements using the same procedure to determine ifthemanipulation has modified the values of the measurements.Incontrast, an observational study does not involveexperimentalmanipulation.Two main statistical methods are used in data analysis:descriptivestatistics, which summarizes data from a sample usingindexes suchas the mean or standard deviation, and inferentialstatistics,which draws conclusions from data that are subject torandomvariation (e.g., observational errors, samplingvariation).Descriptive statistics are most often concerned with twosets ofproperties of a distribution (sample or population):centraltendency (or location) seeks to characterize thedistribution'scentral or typical value, while dispersion (orvariability)characterizes the extent to which members of thedistributiondepart from its center and each other. Inferences onmathematicalstatistics are made under the framework of probabilitytheory,which deals with the analysis of random phenomena.Data collectionSamplingExperimental and observational studiesTypes of dataTerminology and theory of inferential statisticsStatistics, estimators and pivotal quantitiesNull hypothesis and alternative hypothesisErrorInterval estimationSignificanceExamplesMisuseMisinterpretation: correlationHistory of statistical scienceApplicationsApplied statistics, theoretical statistics andmathematicalstatisticsMachine learning and data miningStatistics in societyStatistical computingStatistics applied to mathematics or the arts