This course provides a deeper look into algorithms and data structures from an object-oriented programming perspective. Data structures discussed will include linked lists, stacks, queues, trees, binary search trees, hash tables, heaps and graphs. Concepts such as generic types, iterators, and dynamic programming are also addressed.
Every Spring
Previous: COSC 327
In a data-rich, data-driven society, it is increasingly important to be able to tell a story with data. This course introduces the student to the fundamentals of advanced data visualization techniques, using both interactive computer visualizations and publication ready charts to display data and communicate model results. Whether your interests are related to business or science, effective, accurate, and ethical communication is essential in today’s data-centric world.
Every Spring
Previous: COSC 322
This course provides an introduction to fundamental operating systems concepts. Topics include the process model of computation and concurrent processes, inter-process communication and synchronization, process scheduling, deadlock, memory management, paging and segmentation, and file systems.
Every Spring
Previous: COSC 310
The objective of this course is to teach the student the basic principles involved in the design and operation of computer networks. Topics include computer network architectures and models, physical media and signaling, data link protocols, medium access control, routing and IP, transport services including TCP/UDP, network applications, local-area and wide-area networks. The course will consist of both a lecture portion and a hands-on laboratory.
Every other Interim, odd years
Previous: COSC 360
This course introduces the student to various aspects of artificial intelligence (AI), whose goals are the creation of more useful machines by making them more "intelligent." The course focuses on the fundamentals of machine learning and uses supervised, unsupervised, and reinforcement learning algorithms for classification and prediction tasks. The student will learn to build models from data using regression, logistic regression, classification and regression trees, random forests, ensemble and boosted methods, neural network techniques, and deep learning using convolutional neural networks. These algorithms are then applied to the areas of machine vision, image feature recognition, natural language processing, and general predictive techniques used in the field of Data Science.
Every Fall
Previously: COSC 380