Algorithms and principles involved in machine learning with applications to various engineering domains; fundamentals of representing uncertainty, learning from data, supervised learning, unsupervised learning, and learning theory; design and analysis of machine learning systems; design and implementation of a technical project applied to real-world datasets (energy, images, text, robotics, etc.).We are witnessing an explosion in data - from billions of images shared online to Petabytes of tweets, medical records, and sensor data, generated by companies, users, and the infrastructures around us. Applications of machine learning are increasing rapidly as more techniques are developed and implemented to address a wide range of scientific and societal problems. Many universities are expanding programs in machine learning and perception, and employers are increasingly recognizing the importance of such knowledge. The course will give students a solid foundation in the basics of machine learning and an introduction to the opportunities in this rapidly maturing field.