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 Assessment Cross validati
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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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Syllabus Concepts in Machine Learning- CST 383 KTU

Module-1 (Overview of machine learning)  Machine learning paradigms-supervised, semi-supervised, unsupervised, reinforcement learning. Basics of parameter estimation - maximum likelihood estimation(MLE) and maximum a posteriori estimation(MAP). Introduction to Bayesian formulation. Module-2 (Supervised Learning) Regression - Linear regression with one variable, Linear regression with multiple variables, solution using gradient descent algorithm and matrix method, basic idea of overfitting in regression. Linear Methods for Classification- Logistic regression, Perceptron, Naive Bayes, Decision tree algorithm ID3. Module-3 (Neural Networks (NN) and Support Vector Machines (SVM))   NN - Multilayer feed forward network, Activation functions (Sigmoid, ReLU, Tanh), Backpropagation algorithm. SVM - Introduction, Maximum Margin Classification, Mathematics behind Maximum Margin Classification, Maximum Margin linear separators, soft margin SVM classifier, non-linear SVM, Kernels for learn
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