Foundations of Machine Learning 1

This course aims to introduce machine learning covering traditional methods to recent deep neural network architectures. The course focuses on the underlying mathematical and statistical principles that underpin those methods and how understanding these principles lead to the successful application of machine learning to address practical problems. Examples of suchproblems will be posed for text and image processing. The following topics will be introduced: linear regression, optimization by gradient descent, support vector machines, decision tree classifiers, naive Bayes classifiers, multilayer neural networks, clustering, evaluation of classification and clustering algorithms, cross-validation, and avoiding overfitting. Students will use a standard machine learning library to apply algorithms to various datasets.