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 in Algorithms. Algorithms for Sorting and Searching, Graph Algorithms, Matroid, Linear Programming, Proving NP completeness, 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 introduces concepts, practices, and tools for conducting effective research. You will learn how to read technical papers, interpret published research, assess the research frontier, select research topics, devise research questions and hypotheses, propose and plan research activities, analyze experimental results, and report those results.

An in-depth introduction to the main models and concepts of the mathematical theory of computation, including computability, complexity, and logic. Prerequisites: an undergraduate course in automata theory and formal languages such as COMPSCI 501 or permission of instructor.

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.

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