CS 435 : Embedded Systems

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Introduction to the high-level abstract modeling concepts and the lower-level fundamental programming aspects of real-time embedded systems development. The primary focus is in the design, development and validation of microprocessor-based real-time embedded systems. Course topics will include real-time operating system design, real-time scheduling theory, general-purpose microprocessors, common bus architectures, memory management, device driver development, interrupts, general purpose peripherals: such as timers and counters, I/O subsystems along with some embedded system design problems and engineering issues.

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CS 471 : Artificial Intelligence

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Introduces objectives and techniques used by practitioners and researchers in artificial intelligence. Explores a number of aspects of computational models of intelligence including problem solving (uninformed and informed strategies), game playing, knowledge representation, reasoning, planning, natural language processing, and machine learning. Familiarizes students with the practice of building intelligence systems through a number of hands-on assignments.

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CS 370 : Software Engineering

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Introduces software engineering fundamentals including software development lifecycle, process models, object-oriented methodologies, ethical and professional responsibilities. Covers project management activities such as proposal writing, project planning, scheduling and monitoring, and development activities such as problem domain analysis, requirements engineering, software design, implementation and testing. Establishes semester-long working teams, collaborating on project management activities and development activities to construct mid-sized software products. Produces three project deliverables: Software Requirements Specification Document, Software Design Specification Document, and system executable.

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CS 478 : Introduction to Deep Learning

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Introduces use or application of software to cover fundamental topics of classification, regression and clustering, and a number of corresponding learning models such as the multi-layer perceptron and its gradient-based training through the backpropagation algorithm. Fully connected neural networks will be followed by more specialized neural network architectures such as convolutional neural networks (for images), recurrent neural networks (for sequences), and memory-augmented neural networks.

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CS 351 : Programming Languages

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Important features and concerns of implementation design on programming languages in common use today will be studied and analyzed. Includes data and control structures, run-time storage management, context-free grammars, language translation systems, programming paradigms, and distributed and parallel programming constructs.

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CS 311 : Data Structures & Algorithms

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Understanding several advanced methods for implementing abstract data types and time used by each method. Includes abstract data types such as dictionary, priority queues, matrices, balanced trees, graphs, and relations, as well as, foundation of recursive algorithms, complexity analysis, complexity classes, sorting and searching, computability and undecidability, problem-solving strategies, and heuristic approaches.

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CS 331 : Computer Architecture

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A study of the functional organization and sequential operation of digital computers. The major components of a computer will be discussed. Introduction to machine instruction architecture and design. The study of the internal operations during program execution. Several computer architectures will be studied.

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