Optimization techniques are frequently used in many areas of computer science, in particular in machine learning, to handle a variety of large-scale, data intensive problems. Moreover, algorithmic tools of ever-increasing sophistication are introduced at a fast pace, offering unparalleled opportunities to solve problems efficiently. This class will cover a wide range of optimization methods, including convex, nonconvex and discrete optimization.

This class is an in-depth introduction to systems, focusing on principles of system design that cross-cut numerous systems artifacts, including operating systems, databases, runtime systems, and architecture. We will cover all levels of the "system stack", from chips to distributed systems. This course may be used to satisfy systems core requirements.

The current best testing and analysis practices; emerging new approaches, going beyond the testing of programs to include the testing and analysis of all software artifacts across the lifecycle.

The design and analysis of efficient algorithms for important computational problems. Paradigms for algorithm design including Divide and Conquer, Greedy Algorithms, Dynamic Programming; and, the use of Randomness and Parallelism in algorithms. Algorithms for Sorting and Searching, Graph Algorithms, Approximation Algorithms for NP Complete Problems, and others. Prerequisites: The mathematical maturity expected of incoming Computer Science graduate students, knowledge of algorithms at the level of COMPSCI 311.

This course is intended to serve as an advanced overview of robotics spanning the complete autonomy loop: perception, planning, and control. We will study the theory, algorithms, and efficient implementations related to these topics, with focus on open discussions for how to do research to go beyond the state of the art. Students will gain hands-on experience in implementing, and extending such algorithms using simulations.

Students will work collaboratively to construct production-grade software used to advance the goal of Child Rescue. This course is a group-based, guided independent study. Our goal is to build practical machine learning models to be used by professionals dedicated to rescuing children from abuse. Students will be encouraged to design and build their own diagnostic and machine learning tools, while also learning from professionals in the fields of digital forensics and law enforcement. The entire student group will meet once a week to share progress via short presentations.

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