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Master machine learning for GATE DA
Instructor: Piyush WairaleLanguage: English
Description:
This course is designed to help students prepare for the Machine Learning section of GATE DA . It covers all the essential topics and concepts of machine learning that are relevant to the GATE DA exam.
(i) Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross validation, multi-layer perceptron, feed-forward neural network;
(ii) Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple linkage, dimensionality reduction, principal component analysis.
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