Scope and Objectives of the course: This course is an undergraduate course on Machine Learning. ML is the sub-field of Artificial Intelligence. It helps engineers build automated systems that learn from experiences or examples. It helps machines make data-driven decisions. For example, Google Maps for navigation uses the route network, real-time traffic characteristics, time of travel etc. to predict an appropriate path for you using ML algorithms. ML is a multi-disciplinary field, with roots in Computer science, and Mathematics. ML methods are best described using linear and matrix algebra and their behaviours are best understood using the tools of probability and statistics. According to the latest estimates, 328 million terabytes of data are created daily. With this increasing amounts of data, the need for automated methods for data analysis continues to grow. The goal of ML is to develop methods that can automatically detect patterns in data, and then use the uncovered patterns to predict the future outcomes of interest. This course will cover many ML models and algorithms, including linear models, multi-layer neural networks, support vector machines, density estimation methods, Bayesian belief networks, mixture models, clustering, ensemble methods, and reinforcement learning. The course objectives are the following:

• To select and apply an appropriate supervised learning algorithm for classification problems like Naïve Bayes, SVM, Logistic regression, Neural networks etc., To select and apply an appropriate supervised learning algorithm for regression problems like Linear regression, Ridge regression, Non-parametric kernel regression etc.
• To select and apply an appropriate un-supervised learning algorithm for clustering, linear and non-linear dimensionality reduction etc., To understand ML principles and techniques like Model selection, Under-fitting, Over-fitting, Cross-validation, Regularization etc.
• To test run appropriate ML algorithm on real and synthetic datasets and interpret their results.

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