The use of computational approaches to extract information from vast amounts of data and make intelligent decisions based on that information constitutes the foundation of machine learning, a field that has made a dramatic impact on our daily lives. From medical diagnosis to recommendation systems, language modeling, autonomous vehicles to speech identification, machine learning is now everywhere. This course introduces concepts, issues, and algorithms in machine learning, and will discuss the subject's theoretical and practical aspects. The course will also discuss societal and ethical considerations for machine learning in practice. Main topics of the course will include basic learning theory, supervised, unsupervised and semi-supervised learning, ensemble systems, model selection and combination, feature selection, and performance evaluation techniques. The course will feature assignments and projects that allow students to implement various classical and emerging machine learning algorithms, and evaluate them on real-world applications.