Machine learning is the science of developing statistical methods that quantify relationships within data. This branch of mathematics/computer science has seen an explosive growth over the past decade as our ability to store and process digital data has dramatically increased. Prediction, classification, regression, and identification are the aims of learning from data. All of these problems are routinely performed in data analytics. Topics covered include (1) an overview of the literature in learning-based methods and applications (2) understanding a variety of machine learning techniques for classification, regression, and prediction. (3) obtain the ability to implement and experiment with a wide range of machine learning algorithms in Python with examples. (4) Unsupervised and Supervised learning and clustering concepts, Dimensional reduction, Kernels and kernel-based classifiers such as SVM, and Deep Learning algorithms. (5) To understand and implement learning-based methods for classification of images, signals and features.
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311 Introduction to Machine Learning
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