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Not available at this time

Not available at this time

Not available at this time

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Not available at this time

The course provides an introduction to machine learning algorithms and applications, and is intended for students with no prior experience with machine learning. Machine learning algorithms answer the question: 'How can a computer improve its performance based on data and from its own experience?' The course is roughly divided into thirds: supervised learning (learning from labeled data), reinforcement learning (learning via trial and error), and real-world considerations like ethics, safety, and fairness.

The course explores key concepts of artificial intelligence, including state-space and heuristic search techniques, game playing, knowledge representation, automated planning, reasoning under uncertainty, decision theory and machine learning. We will examine how these concepts are applied in the context of several applications.

The design and operation of modern computer operating systems. Review of capabilities of typical computer hardware. Topics include command language interpreter (the shell), processes, concurrency, inter-process communication, linking and loading, memory management, transactions, file systems, distributed systems, security, and protection. Programming projects in C.