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Course Details

This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighborhood, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will introduce the basics of computational learning theory. In the course we will discuss various issues related to the application of machine learning algorithms.  The course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions.

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