Previous Year Question Papers Concepts in Machine Learning- CST 383 KTU

 

 

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Concepts in Machine Learning- CST 383 KTU Minor Notes- Dr Binu V P

About Me Syllabus Previous Year Question Papers-Concepts in Machine Learning CST 383 Module 1:  Overview of  Machine Learning Bayesian Formulation Maximum a Posteriori (MAP), a Bayesian method/Maximum Likelihood Estimation (MLE), Module 2: Supervised Learning Supervised Learning,Regression, Perceptron Naive Bayes Classifier Decision Trees-ID3 Module 3: Introduction to Neural Networks Neural Networks and activation functions Multi Layer Neural Networks, back propagation Back-propagation Example Activation Functions Implementation of a two layer XOR network with  sigmoid activation Application of Neural Networks Support Vector Machines ( SVM) Module 4:Unsupervised Learning Similarity Measures Representative-based Clustering(K-means and Expectation-Maximization Algorithms) Hierarchical Clustering-Agglomerative Clustering ( AHC) Dimensionality Reduction-Principal Component Analysis ( PCA) Factor Analysis Linear Discriminant Analysis ( LDA) Module 5: Classification Assess...
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Overview of Machine Learning

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To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that should be carried out to transform the input to output. For example, one can devise an algorithm for sorting. The input is a set of numbers and the output is their ordered list. For the same task, there may be various algorithms and we may be interested in finding the most efficient one, requiring the least number of instructions or memory or both. For some tasks, however, we do not have an algorithm—for example,to tell spam emails from legitimate emails. We know what the input is:an email document that in the simplest case is a file of characters. We know what the output should be: a yes/no output indicating whether the message is spam or not. We do not know how to transform the input to the output. What can be considered spam changes in time and from individual to individual.We can easily compile thousands of example messages some of which we know to be spam and what we want is t...
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Supervised Learning-Linear Regression ,Logistic Regression

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Regression Linear Regression Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning. Linear regression was developed in the field of statistics and is studied as a model for understanding the relationship between input and output numerical variables, but has been borrowed by machine learning. It is both a statistical algorithm and a machine learning algorithm. Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables ($x$) and the single output variable ($y$). More specifically, that $y$ can be calculated from a linear combination of the input variables ($x$). When there is a single input variable ($x$), the method is referred to as simple linear regression. When there are multiple input variables, literature from statistics often refers to the method as multiple linear regression. Different techniques can be used to prepare or train the linear regression equation...
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