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Course Criteria
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3.00 Credits
Introduces students to the principal activities involved in developing high-quality software systems. Topics include: requirements analysis, formal specification methods, process definition, software design, and risk management.
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3.00 Credits
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.
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3.00 Credits
Practical experience in the management of software development projects. Managing people, coaching, mentoring, planning, scheduling, evaluation, negotiation, and expectation management are explored. The focus is on the power of communication.
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3.00 Credits
Natural Language Processing techniques developed in Artificial Intelligence with an emphasis on memory oriented models and corpus-driven research. Focus on semantically oriented sentence analysis, narrative text comprehension.
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3.00 Credits
This course will teach students the basics of 3D geometric modeling to create content for virtual worlds, computer games, computer-animated films and other related fields. This course counts as a CS Elective toward the CMPSCI major (BA/BS).
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3.00 Credits
We will introduce neural networks and modeling of brain functions: the inspiration, engineering applications, theoretical analysis, as well as psychological and biological modeling. We will review different neural networks within the two groups: Feed forward networks, applications, and approximation theorem; Recurrent neural networks and computational power; We will also focus on supervised and unsupervised learning with applications in clustering; representability; and Models of memory, diseases, and healthy perception. This course counts as a CS Elective toward the CMPSCI major.
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3.00 Credits
An in-depth introduction to the main models and concepts of the mathematical theory of computation, including computability, complexity, and logic. Prerequisites: Cmpsci 250 and 311, or equivalent.
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3.00 Credits
An advanced course in robotics that covers mechanisms (kinematics and dynamics), actuators, sensors, signal processing, feedback control, and signal processing. The target is to provide a general understanding of sensorimotor systems that can be related to machine learning and other disciplines. We will relate to biological systems whenever possible. Programming exercises will accumulate code for a simulated mobile manipulator and students will pit their "robots" against those of their classmates. pre-requisite: linear algebra, differential equations, and programming skills.
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3.00 Credits
Basic problems in the translation of programming languages focusing on theory and common implementation techniques for compiling traditional (Pascal-like) programming languages to produce assembly or object code for typical machines. Involves a substantial laboratory project in which the student constructs a working compiler for a considerable subset of a realistic programming language within a provided skeleton. Lectures augmented by an optional (but recommended) discussion section covering details of the programming language to be used in the project (Java), the operating system (Unix), the source language (a Pascal subset) and various tools (javacup, make, etc.). Use of computer required.
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3.00 Credits
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 CMPSCI 311.
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