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Course Discription |
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An introductory course in machine learning (ML) that covers the basic theory, applications, and algorithms. ML enables computational systems to learn and find relationships/solutions from observed data. This course balances the theory with the practice. ML has many important applications in data-driven engineering, economics, and analysis of big data. ML is indeed a hot topic. Topics covered in this course include understanding types of learning (supervised/unsupervised/reinforcement); linear classification/regression; model selection and validation; overfitting; the theory of generalizations; training vs testing; least squares; basics in optimization theory/algorithms; neural networks; support vector machines (SVM); kernel-methods; Perceptron Learning Algorithm (PLA); and similarity-based methods (KNN, and Radial-Basis Functions (K-means clustering)). |