Machine Learning algorithms allow computers to be taught to perform tasks without being explicitly programmed. This course is an introduction to machine learning and data mining. The course will explore supervised, unsupervised, ensemble and reinforcement learning. Topics may include: decision tree learning, rule learning, neural networks, support vector machines, Bayesian learning, clustering, hidden Markov model learning, and/or deep learning. The material of this course has some overlap with that of Computer Science 241, but it is permissible to take both.

This course will examine the principles and design choices involved in creating general purpose computer systems. Topics will include instruction set architectures, virtual memory, caching, allocators and garbage collectors, threads and synchronization, file systems, virtual machines, and distributed systems. Projects will involve the implementation and use of these capabilities and abstractions. Students who have taken COSC 261 may not take this course.

This course will be conducted online. Office hours will be available online and in-person as circumstances allow.

“Plague” has multiple origins, so the etymologists tell us. It is associated with stroke, wound, illness, interpreted as divine punishment. “Pandemic,” a word of more recent vintage, relates to “a disease: epidemic over a very large area; affecting a large proportion of a population.” This colloquium will inquire into the current crisis by undertaking a critical history of plagues and pandemics and how they relate to governance and the state. How did we arrive at this moment?