Computing Sciences

Programming fundamentals using the Python programming language: data, variables, selection, loops, arrays, input/output, basic graphics and functions. Object-oriented design: encapsulation, objects, classes and methods.

Information representation and manipulation; file systems and directories; compatibility and data exchange; security and privacy; elements of computer architectures and operating systems; computer networks, the Internet, and the World Wide Web; web site design principles and creation; PC-based examples and illustrations.

No background in computing necessary. Design and implementation of your own database as a group project. Cooperative learning techniques to demystify key concepts: the relational model, normalization, the Entity-Relationship model and SQL.

Computing systems as tools for designing and publishing graphically rich material in many forms; image processing techniques and systems; image style and related tools; digital image processing theory; computational complexity; multimedia presentations.

Object-oriented design: objects, classes, methods, encapsulation; programming fundamentals: data, variables, selection, loops, arrays, input/output; exceptions. EXCLUSION: This course may not be taken by students who have already taken and passed ECE 1620.

Object-oriented design: inheritance, interfaces, polymorphism; problem analysis; recursion; abstract data types; dynamically linked structures; data structures: stacks, queues, lists, collections, trees, maps, priority queues, library collections framework.

Mathematical structures that support computer science: sets, matrices, trees, graphs, logic and proof, mathematical induction, relations, functions, sequences, summations, and elementary combinatorics.

Efficiency classifications and mathematical analysis of recursive and nonrecursive algorithms; design techniques: brute force, greedy, divide-and-conquer, dynamic programming, backtracking, branch-and-bound, space and time tradeoffs; NP-completeness; approximation algorithms; computational problems: sorting, searching, string processing, graphs, arithmetic, linear algebra.

High level language features: data types, control structures; formal lexical and syntactical analysis; operational semantics; language translation.

Skills for success in computing: research questions and methodologies, oral and written presentation techniques, argumentation and critical thinking, time and project management strategies, study skills.

Students will research the effects of the digital divide and implement a program that addresses the needs of underserved high school students preparing for their transition to college.

Fast-paced coverage of object-oriented programming using Java and the Java API, including packages for creating graphics and applets; Java syntax and control structures; arrays; designing objects, classes, and methods; graphical user interfaces; input streams, exception handling and threads.

History of artificial intelligence. Principles of machine learning and training set development. Overview of natural language understanding technology and computer vision technology. Social and ethical issues. Survey of software tools for implementing artificial intelligence. For non-computer science/engineering majors.

Python APIs, internet programming and security, mobile app programming, a team project.

Probability, counting, discrete and continuous distributions, descriptive and inferential statistics, experimentation, reading of scientific papers -- all taught from the point of view of computer science. Some programming may be required.

Architecture of computer systems: representation of data; processor, memory and I/O organization. Assembly language programming. C programming language constructs and their relationship to the underlying architecture. Basics of operating systems: interrupts, concurrency, process scheduling, security, networking.

Processes, threads and concurrent programming. Scheduling and dispatching. Linking and relocation. Memory management. Virtual memory. System-level I/O Device management. File systems. Security and protection in depth. Real-time and embedded systems. System performance evaluation. Scripting.

Internship in computer science involving computer system development, maintenance, or evaluation in an environment which supports sound software engineering techniques. Restricted to second semester sophomore with junior standing and above.

Exploration of the basic concepts and terminology in cybersecurity for students from a variety of technical or non-technical backgrounds; major sub-disciplines of cybersecurity; impact of cybersecurity on business, ethical, and technical levels; data protection concepts, privacy, and intellectual property; data protection techniques, encryption, and authentication; security in wired and wireless networks, operating systems, applications, databases, and the cloud; ethical hacking and vulnerability management; lab exercises and case studies to introduce technical concepts; prepares students to study cybersecurity in-depth in future coursework; course is the introductory required course for a minor in cybersecurity.

Explores the criticality of protecting information's availability, accuracy, authenticity, confidentiality, and integrity. Analysis of topics to include redundancy, backup and recovery, business continuity, security technologies, and controls such as audit, change management and testing.

Theory of game development, game programming, artificial intelligence, state machines, 2D/3D assets, visual communication, game mechanics, navigation meshes, path planning.

Fields, vectors and vector spaces, linear transformations, matrices and matrix algebra, eigenvalues and eigenvectors; Structures and computations in Python for these mathematical objects; Computational applications selected from data science, machine learning, computer vision, graphics, image processing, graph algorithms, quantum computing, information retrieval and web search.

Finite automata and regular expressions; push down automata and context-free grammars; Turing machines; Church's thesis; computability; NP-completeness.

Lexical and syntactical analysis; code generation; error recovery; recursive descent compilation; handling of run-time environment.

Hardware and software in computer graphics; graphics programming language; input/output device handling; modeling in 3D space; development of interactive software.

In-depth study of digital evidence presentation, digital forensic techniques, and data analysis. Password cracking, encryption/decryption, volatile data extraction and network forensics of advanced forensic tools, legal and ethical issues related to forensics and security management techniques.

Concepts and technology of database management systems and data modeling with an emphasis on the relational model; database querying and normalization; physical data organization. A group project to design and implement a database is a key aspect of this course.

History of AI, what it is and isn't; the nature of intelligence and the question of its computer implementation; search algorithms; knowledge representation; automated deduction; machine perception techniques; machine learning; planning; problem solving.

Introduction to machine learning through Python based projects; machine learning tools; supervised and unsupervised learning algorithms and modeling accuracy, bias, and overfitting issues.

The mathematical theory behind the design of software by detecting and extrapolating pattern; neural systems that adapt to new circumstances; decision tree induction; genetic algorithms and genetic programming; research foundations.

The computing parts of the interdisciplinary subject, Data Science. Topics include: data cleaning, data wrangling, visualization, time series, machine learning fundamentals. Program libraries that support managing datasets. Students will complete a major project.

Computer systems that translate human language transformations, such as readability and sentiment analysis, spam filtering, plagiarism detection, and other natural language processing techniques; typically includes collaboration with a non-Computer Science course on team-based student projects.

Operating system structures; system calls; system libraries; interprocess communication; user-interface programming environments; software utilities; software portability.

Management and production of software systems; the software life cycle; software design techniques and methodologies; participation in a team software development project.

Capstone course centered around a semester long sofware development or research project; project planning; requirements elicitation and specification; teamwork; oral presentations required of all students.

Theory and practice of designing apps for mobile devices; interface design, platform-specific and platform-independent programming, sensor-based computing, cloud data management, security, and privacy; entrepreneurial practices for app development: ideation, business planning, and commercialization.

Organization of computer networks; TCP/IP protocols; switching and routing methods; IP addressing and forwarding; TCP flow and congestion control; client-server communication; cryptography: secret-key, public key, digital signatures; security attacks and defenses: authentication and authorization, denial of service, firewalls, VPNs.

Lecture presentation of selected topics in computer science. May be repeated for credit if topics are different. Prerequisites may be imposed depending on the topics.

Reading, research and/or projects in a selected area of computer science under the direction of a member of the staff. May be repeated for credit.

This course explores how software designers and artificial intelligence researchers draw inspiration from biology and learning theory to design programs and robotic agents that learn and adapt to changes in their environment. No prior programming experience is required.