Functional Margin and Geometric MarginWhat is the differenceThe key difference between the functional margin and geometric margin is that the functional margin is defined for every data point, whereas the geometric margin is defined only for the data point closest to the decision boundary. The geometric margin is often used as a measure of the generalization performance of a linear classifier, a..

Discriminative vs Generative LearningDiscriminative learning : How do I separate the classesEstimate parameters of decision boundary directly from labeled examples.→ We can make model p(y∣x)p(y|x)p(y∣x) directly by using datasetsGenerative learning : What does each class look likeModel the distribution of inputs characteristic of the class→ Ultimately, we want to find p(y∣x,θ)p(y|x, \theta)p(y∣..

AbstractWhat is the difference between frequentist and bayesian?FrequentistBayesianWhat is the meaning of prior, posterior, likelihood in Bayesian?So what is the Bayesian inference?Explanation about 4rd procedureExplanation about 5th procedureMaximum Likelihood (MLE)Supervised learning caseFrequentistBayesianInterpretation of MLE (By using KL divergence)Empirical distributionEntropy (Information..

ClassificationIn case of classification, the values y take on only a small number of discrete values.Difference between classification and regressionClassification has only a small number of discrete values. However, regression can have continuous values.Given x(i)x^{(i)}x(i), the corresponding target value y(i)y^{(i)}y(i)is referred to as the label for the training example. (Label represent s..

2_Linear_Regression.pdfBasic Notationx(i)x^{(i)}x(i) : input variablesy(i)y^{(i)}y(i) : Output variables (label)(x(i).y(i))(x^{(i)}. y^{(i)})(x(i).y(i)) : A training example{(x(i),y(i))∣i=i,⋯ ,n}\{(x^{(i)},y^{(i)})|i = i, \cdots, n\}{(x(i),y(i))∣i=i,⋯,n} : A training setX\mathcal XX : The space of input valuesY\mathcal Y Y : The space of output valuesGoal of Supervised LearningThe goal of ..

1_Introduction (1).pdfProbability SpaceDefinition of Probability spaceA probability space is defined by triplet (Ω,F,P):(\Omega, \mathcal F, \mathcal P):(Ω,F,P):Ω\OmegaΩ : Sample spaceF\mathcal FF : σ\sigmaσ-algebra on Ω\OmegaΩP\mathcal PP : F→[0,1]\mathcal F \rarr [0, 1]F→[0,1]Definition of Sample spaceSet of all possible outcomes, where an outcome is the result of a single execution of ..

Era of Artificial IntelligencePersonalized item RecommendationSelf-Driving CarWhat is a machine?Related to ModelFrom what does a machine learn?Related to Training dataHow does a machine learn?Related to Learning AlgorithmWhat is Machine?Hardware or software to perform an intended functionf(x)=yf(x) = yf(x)=yReal examplesHow to make?Traditional paradigmWriting a program codeProblemWe do not have ..

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